Quantum Physics Paper Analysis

Quantum platforms can realize many-body dynamics beyond classical simulation yet complete readout remains intractable: the cost of extracting accessible information scales exponentially with system size. Classical shadows and Bell sampling offer scalable, multi-observable estimation from randomized or entanglement-assisted measurements. Here we aim to push these ideas beyond static snapshots to dynamical correlators, including out-of-time-ordered correlators (OTOCs) and two-point functions. In particular, we introduce the notion of the shadow of an operator, defined as the classical shadow of the vectorized time-evolved operator. Pauli operator-shadows enable simultaneous estimation of all local OTOCs, while Clifford operator-shadows enable efficient simultaneous estimation of all two-point correlators. Alternatively, Bell sampling allows one to simultaneously compute all diagonal OTOCs. We also prove information-theoretic lower bounds for learning OTOCs, fully characterizing their query complexities in many cases, and yielding exponential separations that formalize when the vectorized approach provides measurement-efficiency advantages.

As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.

The ability to generate and detect quantum states of light on a single integrated photonic device is essential to scale quantum photonics into useful quantum technologies. Integrating the required capabilities into complementary-metal-oxide-semiconductor compatible monolithic chips can reduce cost and unlock new functionality through miniaturisation. In this work we demonstrate a single silicon-on-insulator photonic integrated circuit for the monolithic generation and detection of quantum light on a commercially available platform that operates entirely at room temperature. Specifically, we leverage spontaneous four-wave mixing in silicon waveguides to produce squeezed light which is subsequently detected by photodiodes operating in a pulsed homodyne detector configuration on the same chip as the source. We directly measure $0.25(1)$ dB of squeezing, including contributions from waveguide propagation loss and detection inefficiency, and include a detailed analysis of the impact of nonlinear loss on the squeezing levels achievable using this platform.

We characterize and compare the inherent interpretability offerings of a standard linear model with a single qubit mixed state model for the task of supervised binary classification. A side by side comparison reveals that a single qubit mixed state model for binary classification is just the ``ellipsoid version" of standard linear model classification. More precisely, rather than learning a hyperplane to classify data, we learn a hyperellipsoid. We discuss the consequences of the geometric inductive biases of both models, as well as how each model contains a different feature importance inductive bias. This short characterization offers an accessible route to quantum machine learning (ML) ideas for readers who have zero background in quantum and are only familiar with linear classification in ML. In support of ML pedagogy, we encourage instructors to utilize this piece to smoothly introduce quantum ML ideas into the undergraduate ML classroom.

Deep thermalisation characterises the emergence of universal quantum state ensembles on subsystems due to projective measurements on their complement. We study the notion of locality, or lack thereof, in this phenomenon by considering a subsystem partitioned into two disjoint subregions which remain causally disconnected at all times under unitary dynamics. We show that the onset of deep thermalisation in this geometry is fundamentally bounded by measurement-induced entanglement teleportation between the subregions. While measurements on the environment generate entanglement across the disconnected partitions -- suggesting an apparent non-locality -- we demonstrate that generic locally interacting systems exhibit an emergent locality. Specifically, the timescales for both deep thermalisation and entanglement teleportation scale logarithmically with the distance separating the subregions. Exceptions to this include special circuits where the randomness of the measurement outcomes is perfectly transmitted to the ensemble of states of the subsystem, conditioned on the outcomes; in such cases the timescale for deep thermalisation is finite leading to genuine non-locality.

Dynamic entanglement distribution is a key requirement for scalable, multi-user and multi-protocol quantum networks. We demonstrate a metropolitan-scale entanglement-based quantum communication network enabled by a quantum reconfigurable optical add-drop multiplexer (q-ROADM), which dynamically distributes polarisation-entangled photon pairs from a broadband source to six users over deployed campus and metropolitan fibre. The network supports programmable full-mesh, partial-mesh and sliced sub-network configurations, enabling flexible allocation of entanglement resources according to link condition and service requirement. We demonstrate stable six-user full-mesh operation over more than 150 hours, compare full-mesh and time-shared partial-mesh strategies under different source and detector conditions, and realise quantum network slicing with optional/additional interconnection links. We also show that the same infrastructure can support different quantum protocols by showcasing Secure Inaugural Authentication-Transfer (SIAT) combined with Network flooding over multiple paths to improve the security of onboarding a new user. These results demonstrate a q-ROADM-enabled entanglement distribution architecture as a novel route towards reconfigurable, service-oriented quantum networking over optical fibre infrastructure.

A critical limitation of solid-state quantum devices arises from the materials from which they are fabricated: uncontrolled surfaces, interfaces, and structural imperfections introduce numerous sources of loss and decoherence. Despite extensive efforts, linking these decoherence mechanisms to their microscopic material origins -- essential for developing effective mitigation strategies -- remains an outstanding challenge that has slowed coherence improvements. Here, using scanning gate microscopy on live superconducting circuits we identify a previously unrecognised decoherence mechanism originating from Coulomb blockade and microwave-driven charge tunnelling in metallic grains. Such grains are ubiquitous in thin-film devices fabricated by standard lithography processes. By characterising multiple defects across different devices, we find such defects to be as common and as debilitating to device performance as two-level system (TLS) defects, while originating from a fundamentally different physical mechanism. Importantly, conventional characterisation techniques would misattribute this loss to other, microwave power-independent processes. Our observations thus reveal a widespread source of decoherence in superconducting circuits, challenging the prevailing paradigm that coherence lifetimes are primarily limited by TLS defects. Eliminating metallic grains during fabrication provides a clear and practical route to suppress this mechanism, offering a pathway towards improved coherence and reduced noise in microwave-based solid-state quantum devices.

Additivity of minimum output entropies is a central problem in quantum information theory. Nonadditivity is known for every Rényi order $p>1$, at the von Neumann point $p=1$, and near $p=0$, while most of the interval $0<p<1$ has remained open. We prove that for every Rényi order $p$ satisfying either $p>3/4$ or $0\leq p<1/4$, there exist finite-dimensional projection-induced quantum channels such that additivity of the minimum output $p$-Rényi entropy fails. The proof combines two correlated random-projection constructions: a product-conjugate Bell-state witness for $p>3/4$, and a transpose-complement rank-defect witness for $p<1/4$. Thus the unresolved part of $0<p<1$ is reduced to $[1/4,3/4]$. Our estimates also improve the output dimension threshold for additivity violation of minimum output von Neumann entropy, first established in Belinschi, Collins and Nechida.

Quantum entanglement is the fundamental hallmark of quantum mechanics and a core resource for realizing long-distance quantum communication and scalable linear quantum computing. Accordingly, the precise detection and quantitative quantification of entanglement constitute a foundational and critical problem in quantum information theory. To date, researchers have proposed numerous sufficient conditions for entanglement detection as well as a variety of entanglement measures to characterize the entanglement strength of quantum states; nevertheless, efficient and direct measurement schemes for core entanglement parameters remain underdeveloped. Based on unitary transformations and auxiliary measurements, this paper proposes a set of quantum circuit schemes capable of directly measuring the bipartite concurrence and the tripartite 3-tangle entanglement measure. By introducing auxiliary qubits and constructing specific controlled unitary operations, the proposed scheme maps the analytical expressions of the two entanglement measures onto the measurement probabilities of output states from quantum circuits. It enables efficient and direct quantitative measurement of bipartite and tripartite entanglement without performing full quantum state tomography. This work provides a feasible technical route for the experimental characterization of entanglement properties and lays a groundwork for the practical deployment of multipartite entanglement resources in quantum information processing.

We develop a backpropagation algorithm for evaluating parameter gradients in quantum circuits using Pauli propagation simulation. The method has computational complexity comparable to that of standard sparse Pauli simulation techniques, while producing gradients whose accuracy is of the same order as the corresponding observable expectation values. By exploiting the reversibility of quantum circuits, the algorithm reduces the memory cost by a factor of $\mathcal{O}(n_\text{param})$ compared with conventional reverse-mode automatic differentiation, where $n_\text{param}$ denotes the number of parameters in the circuit. Compared with finite difference methods, the algorithm is $\mathcal{O}(n_\text{param})$ more efficient in function evaluations. These features enable efficient and accurate classical optimization of quantum circuits for applications such as state preparation and time-evolution compression, while also allowing operator-complexity measures such as the operator stabilizer Rényi entropy to be monitored and regularized during optimization. We demonstrate the method by optimizing low-energy state-preparation circuits for transverse-field Ising models in one, two, and three dimensions and for the three-dimensional Heisenberg model, and by compressing two-dimensional time-evolution circuits.

Dissipative preparation of quantum order offers a route to superconductivity that does not rely on enhancing attractive interactions. Here we propose a driven-dissipative protocol to prepare superconductivity as a stationary state of a two-dimensional moiré heterostructure. The key ingredient is a bilayer moiré platform in which the layer degree of freedom acts as a pseudospin, allowing the pseudospin structure required for pairing to be implemented through optically induced spatial operations. This preparation scheme requires local dissipation, which we show to arises naturally from weakly dispersive bosonic modes in the heterostructure. In contrast, in the opposite regime of collective dissipation, the same platform exhibits an early-time superradiant burst. Our results establish driven-dissipative moiré heterostructures as a promising platform for preparing superconductivity, while also revealing a connection between steady-state pairing and transient superradiance.

Using a robust photon loss model for photonic quantum experiments, we derive $n-$th order parameter-shift rules for computing gradients of Fock boson sampling transition probabilities where $n$ photons are sent into a lossy interferometer. We also show that in general it is not possible to generalize this gradient recipe to the case of Gaussian boson sampling. Only in the specific case where the transmission matrix of the interferometer can be factorized as a diagonal loss matrix premultiplied by a pure unitary it is possible to obtain parameter-shift rules with a finite order given by twice the total number of photons detected. We demonstrate the efficacy of the proposed method by comparing its performance against finite differences on real hardware.

We construct a new family of Calderbank-Shor-Steane (CSS) codes using the generator and parity-check matrices of Low-Density Generator Matrix (LDGM) codes, with row operations applied to both matrices in order to achieve the desired quantum rate. Decoding is performed in an iterative manner, by applying message passing over the associated graph, and discrete Density Evolution (DDE) is used to optimize performance in the depolarizing channel. The proposed construction offers high flexibility and easiness in the design, producing quantum codes that possess excellent error correction capabilities. By properly designing the structure of the code, we are able to control and bound the weight of the stabilizer generators to a small value, which results in codes particularly well suited for fault-tolerant quantum computation. At the same time, these codes achieve very good performance in terms of error correction capability.

The corner transfer matrix renormalization group (CTMRG) is one of the standard contraction methods for infinite projected entangled-pair states (iPEPS), but its computational cost is dominated by repeated truncated singular value decompositions (SVDs). We introduce subspace-iteration CTMRG (SI-CTMRG), a QR-based projector construction that replaces each large-matrix SVD with an SVD of a much smaller matrix. The resulting algorithm shifts the dominant cost from decompositions to tensor contractions, making it highly suited to GPU acceleration and yielding speedups of up to two orders of magnitude over standard CTMRG. We demonstrate the efficiency and accuracy of the method for the triangular-lattice Heisenberg antiferromagnet, reaching state-of-the-art iPEPS results on a single H100 GPU in approximately 10 hours of computation.

The entanglement-assisted capacity of a quantum channel admits an additive single-letter characterization, implying that joint encodings across channel uses cannot increase the ultimate communication rate. Here, we show that this additive picture does not extend to communication reliability. Specifically, we prove that the Petz-Rényi channel information can be strictly superadditive for every $α\in[1/2,1)$, yielding a genuine multi-copy enhancement of the entanglement-assisted random-coding error exponent, even though the entanglement-assisted capacity remains additive. We establish this phenomenon analytically already for measurement channels, which are entanglement-breaking and have additive unassisted capacity. Remarkably, this strict superadditivity is witnessed by a separable, classically correlated two-copy channel-input marginal, demonstrating that no entanglement between the transmitted systems is required. Our results show that, although correlations across channel uses cannot increase the ultimate rate of entanglement-assisted communication, they can enhance its reliability.

Quantum many-body scars provide an exception to generic thermalising dynamics, but their relation to integrability remains unclear. Here we apply the periodic-orbit framework to the integrable XYZ/XXZ spin-1/2 chain, developing an energy-resolved approach that enables us to locate and track periodic orbits across the spectrum. We identify families of scarred orbits and resolve their supporting eigenstates in terms of Bethe-ansatz quantum numbers, revealing a common string core dressed by zero-momentum magnon pairs. This allows us to reconstruct both the scarred eigenstates and the associated towers directly within the Bethe ansatz, and explain the equidistant tower spacing analytically from the decoupling of zero-momentum magnon pairs in the Bethe equations, providing a microscopic realisation of scar phenomenology in an integrable setting. We further show that this structure persists upon breaking integrability with a transverse field, with the periodic orbits continuing to govern the dynamics. Our results establish a direct connection between the periodic-orbit picture of quantum scarring and the algebraic structure of integrable models, showing that key features of scar dynamics can be understood analytically within the Bethe ansatz framework.

The concept of finite entanglement scaling forms one of the pillars on which the tensor network ecosystem is built. In this paper, we resolve the open problem of determining the actual perturbations induced by matrix product state approximations of critical systems, and we demonstrate that these can be quite different than the ones predicted by conformal field theory. To that aim, we develop a sparse linear solver to calculate the forward and backward derivatives of 2-dimensional tensor networks with respect to their defining parameters in an implicit way. This algorithm is of independent interest as it provides a primitive for the variational optimization of projected entangled pair states that circumvents the instabilities plaguing traditional automatic differentiation methods.

In recent years, quantum lock-in detection has emerged as a promising technique to accurately detect weak signals submerged in background noise. However, the signal-to-noise ratio of existing protocols is severely limited by spectral leakage resulting from control operations implemented in pulse form. Here, we propose a general protocol for realizing quantum lock-in detection by employing successive quantum adiabatic evolution. In our protocol, the signal modulation is achieved by adiabatically controlling the time evolution of the quantum probe, which enables the implementation of triangular modulation functions. The realization of triangular-wave modulation fundamentally solves the problem of spectral leakage and facilitates the extraction of the complete characteristics of the target signals. We present a practical implementation scheme of adiabatic quantum lock-in detection based on nitrogen-vacancy centers in diamond, and demonstrate that the proposed protocol possesses strong resilience against experimental imperfections. Our results establish adiabatic quantum lock-in detection as a robust and experimentally accessible approach to detection of weak alternating signals in noisy environments, thus promoting the advance of real-world quantum sensing technologies.

Magnetic monopoles lie at the crossroads of gauge fields, topology, geometric phases, and charge quantization, yet they remain elusive as fundamental particles. Here we show that an emergent Dirac-monopole framework arises naturally from the dynamics of massive quantum vortices in a spherical superfluid shell. Their dynamics is formally equivalent to that of interacting charged particles constrained to a sphere in the field of a magnetic monopole. The monopole charge is fixed by the superfluid density and automatically satisfies Dirac's quantization condition. The emergent monopole description predicts cyclotron-like vortex motion, in quantitative agreement with Gross--Pitaevskii simulations. We further show that topological frustration induced by two like-charged polar vortices gives rise to the formation of an equatorial vortex necklace, a configuration reminiscent of the polygonal cyclone clusters observed around Jupiter's poles, before its subsequent breakup through a Kelvin--Helmholtz-like instability. Within this framework, the vortex necklace may be viewed as a quantized analogue of Wu--Yang gauge patching. Our results establish spherical superfluids as a versatile platform for realizing and exploring fundamental aspects of Dirac-monopole physics.

We employ a mathematically equivalent form of the GKLS master equation to arrive at an exact theoretical description of a two-level system strongly coupled to the environment. The framework, while intuitive, shedding light on the physics of the problem, and agreeing with existing results, such as thermalisation to a non-canonical state, is based around three parameters that are unknown outside of the weak coupling regime -- the analogue to the detailed balance relation, and two coupling strength constants. As a way forward, we propose a feasible experimental protocol based on a solid-state electronic quantum dot device, through which the fundamental parameters of the problem can be revealed, which would further the fundamental understanding of strong coupling.

We study time-discrete parametric approximations of evolution equations in Hilbert spaces based on residual minimization. The solution is represented by a parametrized ansatz belonging to a low-dimensional nonlinear manifold, and time stepping is performed by minimizing suitably defined residuals at each step. Two natural residual formulations are considered: discretization followed by parametrization of the evolution equation, and discretization of the Dirac--Frenkel variational principle governing the parameter dynamics. A unified error analysis is developed for both approaches within the family of $ζ$-methods. The resulting bounds separate the effects of time discretization from those of residual minimization and yield first- and second-order convergence under Lipschitz, one-sided Lipschitz, and dissipativity assumptions. For the variational formulation, additional stability conditions involving the conditioning of the parametrization map arise naturally. The framework is applied to Gaussian approximation manifolds, for which residual norms and gradients admit explicit closed-form expressions when polynomial operators are involved. This enables efficient implementation without spatial discretization. Numerical experiments for time-dependent Schrödinger equations illustrate the theoretical convergence rates and the influence of residual accuracy on conservation properties.

Subspace Quantum Diagonalization (SQD) recovers ground-state energies by classically diagonalizing a Hamiltonian in the subspace spanned by quantum samples, requiring only bitstrings with sufficient ground-state overlap rather than an accurate variational energy. We reveal and exploit this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades? We answer through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle. Both of these techniques can be applied to a VQE ansatz on a qubit-reduced Hamiltonian. A systematic ablation study across 21 molecules shows that median SQD error stays within chemical accuracy even at 50\% compression on both axes, while simulation speedup reaches $33\times$. Hardware validation on 6 molecules on IBM quantum hardware confirms up to $2.8\times$ transpiled-depth reduction with zero loss in SQD accuracy.

We investigate the Casimir effect for a massive real scalar field confined between two perfectly reflecting parallel plates in the presence of a position-dependent effective mass, a mechanism for coupling a scalar background to a scalar field. Exact normal modes are obtained by solving the corresponding Klein-Gordon equation, leading to a transverse energy spectrum that exhibits a characteristic Landau-like structure despite the absence of an external magnetic field. Upon quantization of the field, the vacuum energy is evaluated by means of generalized zeta-function regularization together with an appropriate renormalization procedure. The renormalized vacuum energy naturally separates into a Landau-like contribution and an additional term induced by the spatial dependence of the effective mass. We show analytically and numerically that both contributions are exponentially suppressed in the strong-coupling regime. In the opposite limit, the Landau-like contribution smoothly reproduces the standard vacuum energy for a massive scalar field confined between parallel plates, whereas the additional contribution becomes singular owing to the restricted domain of validity of the exact spectrum. Except in the vicinity of this singular limit, the vacuum energy is shown to be dominated by the Landau-like sector. Our results establish a direct connection between position-dependent effective masses and boundary-induced quantum vacuum phenomena, providing a new exactly solvable framework for the investigation of Casimir effects in spatially inhomogeneous relativistic systems.

Low-overhead quantum error-correction schemes are essential for enabling quantum computation on registers containing multiple logical qubits. For planar architectures with limited nearest-neighbor qubit connectivity, the surface code has emerged as the leading paradigm. Recent theoretical and experimental work has shown that a physical-qubit connectivity of degree three is sufficient to implement fault-tolerant quantum error correction. In this work, we study lattice surgery in the context of such trivalent architectures and introduce scalable circuit constructions to implement it. Compared with the four-valent measurement scheme, the trivalent lattice-surgery protocol reduces the required resources by $\mathcal{O}(d)$ qubits out of a total qubit count of $\mathcal{O}(d^2)$ and by $\mathcal{O}(d)$ two-qubit gates out of a total two-qubit gate count of $\mathcal{O}(d^3)$. We benchmark the logical fidelity of both lattice-surgery schemes in terms of experimentally realistic simulations targeting an implementation with a fluxonium qubit based architecture and find a potential improvement of up to $\approx25\%$ for distance-three. These results open a way for scalable planar trivalent qubit architectures to host a surface-code-based logical quantum processor.

We propose a machine-learning-empowered approach to the quantum sensing of the plaquette phase, a gauge-invariant quantity arising in three-level $Δ$ systems. This phase profoundly affects the system dynamics, breaking coherent population trapping and inducing a non-trivial phase dependence of the dynamics. We demonstrate that a multi-layer perceptron (MLP), trained in a supervised-learning framework, can accurately estimate the plaquette phase from STImulated Raman Adiabatic Passage (STIRAP) population transfer efficiencies measured under different driving conditions, which provide experimentally accessible observables. Our results highlight how the combination of coherent control and machine learning (ML) enables effective phase identification in closed-loop quantum systems, opening new perspectives for quantum technologies, specifically quantum sensing applications including synthetic gauge fields.

The current leading approach to the variational optimization of projected entangled-pair states (PEPS) is based on automatic differentiation, which allows for a convenient evaluation of the energy gradient with respect to the local variational degrees of freedom. However, evaluating the energy gradient not only remains a major computational bottleneck of the optimization procedure, but also suffers from frequent numerical instabilities. In this work, we adopt recent advances in implicit differentiation techniques to address these challenges in PEPS optimization. By reformulating the core step of the gradient computation in terms of a single characteristic equation for the contraction environment, we reduce the cost of the gradient computation and improve its scaling with the problem size. By choosing a suitable parametrization of this characteristic equation based on the intrinsic symmetries of the contraction environment, we can directly remove instabilities from the global gradient computation that would otherwise arise from the derivatives of subroutines of the contraction algorithm. Finally, we demonstrate how this approach drastically simplifies the practical implementation of stable gradient-based PEPS optimization.

Stabilizer codes are often constructed within the Calderbank--Shor--Steane (CSS) framework, where two mutually orthogonal binary classical codes define $X$ and $Z$-type stabilizer generators. While this structure is algebraically convenient, additional non-CSS constraints may help suppress low-weight logical operators and improve decoding performance in the finite-length regime. We thus introduce quantum XYZ stabilizer codes, whose parity-check matrix (PCM) is built from three pairwise orthogonal binary PCMs associated with $X$-, $Y$-, and $Z$-type stabilizer generators. A nontrivial point is that an XYZ code instance is not automatically genuinely non-CSS: the same stabilizer group may admit a CSS generating set. We characterize this collapse, obtaining algebraic and rank conditions for deciding when the $Y$-type checks are redundant and when they define genuinely non-CSS stabilizer constraints. We also derive upper and lower bounds on the quantum minimum distance, including bounds for mixed Pauli logical operators. The novel framework includes a known non-CSS topological code, namely the XYZ$^2$ hexagonal code, and yields also sparse finite-length quantum low-density parity-check (qLDPC) constructions from intersecting-subset and quasi-dyadic code families. Simulations under depolarizing code-capacity noise and quaternary belief propagation decoding show that the proposed XYZ qLDPC instances can outperform representative CSS qLDPC instances with similar finite-length parameters.

We study a finite-time quantum information engine in which a two-level system is measured by a quantum harmonic oscillator acting as a meter and where useful work is extracted conditionally on the measurement outcome. Using multi-objective optimisation, we find a Pareto-optimal trade-off between extractable work and its fluctuations and show that reducing fluctuations entails higher thermodynamic costs: greater information consumption, more engine cycles, longer operation time, and reduced average work output. In the limit of a highly accurate meter, we obtain the work distribution, its moments, and the Pareto front analytically. In this regime, the work statistics of the engine reduce to those of a qubit in contact with a single thermal bath. We further analyse the associated information flows by examining the mutual information and Fisher information, and show that the Pareto-optimal engine designs lie very close to local maxima of the latter with respect to the operation time of the device. Our results provide a compact description of the trade-offs between work, its fluctuations, and thermodynamic costs in quantum information engines.

Gleason's theorem identifies the Born rule via non-contextuality over an infinite continuous lattice of projectors, while its corollary the Kochen-Specker (KS) theorem rules out the specific class of deterministic ($\{0,1\}$-valued) noncontextual models using finite sets of projectors. Gleason's theorem also indicates the existence of KS-type finite vector constructions to rule out other discrete nondeterministic alternatives to quantum theory beyond the $\{0, 1\}$ case. Here, we construct finite measurement configurations to rule out noncontextual empirical models with outcome probabilities drawn from an arbitrary finite subset $O \subset [0,1]$. We do this by two means: (i) constructing a family of experimentally feasible state-dependent Hardy-type tests, and (ii) proving a generalized KS theorem for a broad class of $O$ that includes prior results as special cases. In the sheaf-theoretic framework of Abramsky and Brandenburger, a hierarchy of probabilistic-possibilistic-strong contextuality has been established quantifying contextuality as a resource. We extend this framework by introducing strong $O$-valued contextuality, showing that quantum theory evades global sections of all finite-valued presheaves. We also discuss the implications of the result on finite many-valued logics as viable ontological models for quantum theory and for contextuality-based (semi)-device-independent protocols.

We construct the three-point continuous-variable (CV) quantum MacWilliams identity, extending the two-point framework of Burchards, and give its closed-form integral kernel. Its configuration space carries a symplectic invariant with no classical counterpart, which encodes the GKP quantization condition and a three-point sign phase. Using the identity, we derive the semidefinite-programming bounds it supports on the dimension of CV quantum error-correcting codes, and we prove, in two collapse theorems, that the three-point apparatus does not improve on the two-point bound. For GKP lattice codes the three-point optimum equals the Burchards two-point linear-programming optimum identically. This is an exact determination of the lattice three-point optimum, so the $E_8$ and Leech magic functions saturate it rather than beat it. For general bosonic codes a completely-positive reformulation bypasses the positivity obstruction that rules out the natural factored-form constructions; the phase-sign condition together with Choi positivity then force the three-point term to vanish. We certify this collapse for radial Choi forms on the first eight Laguerre levels at one mode, and leave the full trace-class cone open. Both collapses have a single cause with no classical analogue, the code projector: it orients the bound correctly but also removes the full positivity that powers the classical three-point improvement.

We study a duality analysis in conjunction with the star-triangle transformation for symmetric-group permutation models on the triangular and honeycomb lattices. The calculation is motivated by the permutation-model description of random tensor networks and by earlier duality analyses of replicated spin glasses. The essential point is that the finite-basis unit is not a bare bond but a star-triangle block. Our analysis estimates the critical bond dimension for the honeycomb lattice to be 2.634929344884, and the associated single-bond duality relation yields the triangular-lattice estimate 1.475661534848.

Quantum gate design is often represented as pulse optimization, although the physical object that implements a gate is the full controlled evolution generated by the pulse. Here we use physics-informed neural networks to represent single-qubit gate design at this evolution level: the control fields, the Bloch-state trajectories, and the total duration are learned together under the Bloch equation. This changes the optimized object from pulse amplitudes to a differentiable physical process whose structure can be inspected and refined. For rotation gates, the optimized evolutions recover the physical organization expected for bounded single-qubit control, with no prescribed pulse ansatz or duration scan. For a geometric gate, the representation identifies localized bottlenecks in maintaining the geometric condition and turns this diagnosis into feedback, reducing the residual path error while preserving high fidelity. Thus physics-informed learning is used not only to synthesize gates, but also to make optimized quantum controls physically readable, diagnosable, and locally refinable. This process-level view may be especially useful for adapting gates to hardware-specific, task-specific, and locally varying experimental constraints.

Exact fermion to qubit transformations are conventionally regarded as algorithmic tools that translate many-body Hamiltonians into qubit representations for quantum simulation. Here we show that they also define intrinsic geometric representations whose structure encodes physically meaningful information beyond spectral equivalence. We develop a geometric framework based on weighted hypergraphs and coupling space representations constructed from the Bravyi--Kitaev (BK) and Xia--Bian--Kais (XBK) encodings. Within the BK representation, we introduce a geometric observable that compares the algebraic connectivities of the kinetic and interaction hypergraphs, derive its exact analytical dependence on interaction strength, and uncover two geometric universality classes together with an exact spectral organization originating from the binary tree architecture of the encoding. The complementary XBK representation describes the evolution of encoded Hamiltonians through probability measures in coupling space, where optimal transport quantifies interaction-driven reorganization independently of the spectral analysis. Applications to the Hubbard, spinless tV , single impurity Anderson, and Kitaev models demonstrate that these connectivity and transport based geometric descriptions consistently capture the structural evolution of encoded quantum Hamiltonians across distinct classes of many-body systems. Our results establish hypergraph geometry as a new framework for understanding fermion-to-qubit encodings,revealing that they serve not only as computational mappings but also as geometric representations of quantum many-body Hamiltonians.

We give the definitive proof that the Dirac Quantum Cellular Automaton (QCA) used for both quantum simulation and algorithmic foundations of Quantum Field Theory (QFT), and especially of Quantum Electrodynamics (QED), as put forward in References https://doi.org/10.1007/s11128-019-2555-4 and https://doi.org/10.22331/q-2023-11-08-1179, does exhibit Fermion Doubling (FD), albeit thrice as less severe as discrete-time standard Lattice Gauge Theories (LGTs) -- as shown in Reference arXiv:2505.07900 -- , which are naive regarding the spacetime discretization of differential operators acting on fermionic fields. The proof is done for the $(1 + 1)$D Dirac-QCA model. We show that the (one-time-step) two-point correlation function, also called Green's function (GF), of the Dirac QCA, is of astonishing simplicity, which is in contrast with the GF of the Dirac equation. We also compare, both qualitatively and quantitatively, this Dirac QCA to the continuous-time-LGT spatial discretization of Dirac fermions regarding how well these two lattice models approximate their naive continuum limit -- which is nothing but the Dirac equation -- even when far away from that limit, a situation which must be considered because of experimental limitations in quantum simulation -- : the Dirac QCA is better for ultrarelativistic regimes, whereas continuous-time LGT is better for non-relativistic regimes. In a second part of this work, we compute the GF of the FD-fixed model put forward in the last cited reference, called Flavored Dirac QCA (FQCA) -- which staggers an extra, artificial flavor \emph{only}, on a diamond spacetime lattice, and does not stagger chirality as staggered fermions in usual LGT. The structure of this FQCA two-point correlation function is of extreme simplicity, and can be expressed in a very simple manner in terms of the four chiral components of the FD-suffering, original-model GF.

We establish operational limits on the broadcasting of genuine multipartite entanglement (GME) in quantum networks. Using a distributed protocol in which each of N parties locally implements optimal 1 $\to$ 2 cloning via beam-splitter interactions, we derive exact expressions for the broadcast fidelity of Greenberger-Horne-Zeilinger (GHZ), W states, and Cluster states in a limited setting. We show that the fidelity decays exponentially with system size as [c(R)]$^N$, providing a quantitative expression of multipartite entanglement monogamy in the broadcasting setting, and that both state families share a universal normalisation factor arising from independent post-selection probabilities. Most significantly, we prove a no-go result for simultaneous GME certification: for all reflectivities and all system sizes, the two broadcast copies cannot be simultaneously certified as genuinely multipartite entangled within the standard framework of fidelity-based witnesses. We further check this behaviour for three- and four-party cluster states, finding consistent results that support the generality of the no-go beyond the GHZ and W families. This obstruction arises from the redistribution of multipartite coherence, which both reduces the achievable fidelity and increases the corresponding certification threshold. Our results reveal a fundamental trade-off between the broadcastability of multipartite entanglement and its operational certifiability, and delineate intrinsic limits on entanglement distribution in quantum networks.

Gauge theories and quantum error-correcting codes share the same underlying structure: both use constraints to identify a specific subspace of the full Hilbert space. In quantum error correction, these constraints are known as stabilizers, while in gauge theories they correspond to Gauss law. In this work, we consider a family of discrete Abelian lattice gauge theories described by a $\mathbb{Z}_{N}$ gauge group with $N$ an arbitrary power of two. In this setting, we find a set of stabilizers for the gauge-invariant subspace which is an alternative to the Gauss operators, and we call them binary Gauss stabilizers. We use this alternative stabilizer group to build practical error-correcting codes exploiting the gauge symmetries of the system without the addition of extra qubits. The applications of our finding are not limited to error correction though. We also provide a new strategy of gauge fixing to remove the redundancies based on our alternative stabilizer, which might provide advantages with respect to already-existing approaches such as the axial gauge. Our results provide new tools to study lattice gauge theories and their quantum simulation, and opens directions for future work at the interface of lattice gauge theory and quantum information.

Quantum remote control, also known as quantum remote implementation of an operator (QRIO), enables the remote manipulation of an arbitrary quantum state by implementing a desired quantum operation at a distant location. Significant progress has recently been made in developing QRIO protocols and their variants. Most existing schemes employ hyperentangled states where entanglement is shared across multiple degrees of freedom (DOFs). However, these protocols typically exploit only one degree of freedom at a time. In this work, we propose a QRIO protocol that simultaneously utilizes the polarization and spatial DOFs of a two-qubit hyperentangled state to remotely implement an arbitrary hybrid operator on an unknown single-photon two-qubit hyperstate. The shared hyperentangled resource is realized using the polarization and spatial modes of photons, while the protocol is constructed using linear optical elements and cross-Kerr nonlinear interactions to facilitate effective photon-photon coupling. Furthermore, the effects of measurement errors arising from finite coherent state distinguishability and coherent state dissipation are analyzed and the corresponding success probability of the protocol is evaluated. The results demonstrate that an appropriate choice of the cross-Kerr phase shift and coherent state amplitude significantly enhances the protocol performance, making the proposed scheme a promising candidate for hybrid quantum communication and distributed quantum information processing.

We study the effect of magnetic vector potentials on periodic Grover walks on finite graphs. The magnetic vector potential is introduced through the framework of quantum graphs, which induces the Grover walk as a special case. We regard the vector potential as a perturbation of a periodic Grover walk and investigate the robustness of its periodicity. Our analysis reveals that the response to such perturbations depends on the spectral structure of the underlying graph. In particular, when the graph possesses at least one non-simple eigenvalue, we derive a Hermitian matrix that characterizes the robustness of its periodicity. As a consequence, we show that the perturbed dynamics is asymptotically described by a continuous-time quantum walk generated by this Hermitian matrix.

Programmable photonic interferometer meshes enable reconfigurable linear optical transformations, but their performance depends critically on accurate calibration of Mach-Zehnder interferometers and phase shifters. Conventional methods often require node isolation, dedicated routing paths, orthogonal training states, reference channels, or prior phase-voltage characterization, which become increasingly difficult in large thermally tuned meshes. We introduce a local variance-based self-calibration method using intensity-only measurements. Controlled phase perturbations are applied, and calibration points are identified from minima of the measured output-power variance. For Mach-Zehnder interferometers, the variance follows a characteristic |sin(theta)| dependence, allowing bar and cross operating points to be found without conventional node isolation. For phase shifters, balanced interference produces a complementary |cos(phi)| variance signature, enabling quadrature calibration through the same statistical principle. We validate the method experimentally on an 8 x 8 silicon nitride programmable photonic processor using a fully automated two-stage procedure. Starting from random phase settings, all Mach-Zehnder interferometers are calibrated first, followed by phase-shifter calibration under balanced-interference conditions. As a system-level test, we implement an embedded 4 x 4 Hadamard transformation on the 8 x 8 processor using a Clements decomposition. These results establish local output variance as a simple calibration observable for programmable photonic meshes. The method is compatible with discrete random phase ensembles and requires neither conventional node isolation nor orthogonal training fields, making it a practical calibration primitive for scalable self-stabilizing photonic processors.

The Navascués-Pironio-Acín (NPA) hierarchy provides a convergent sequence of semidefinite programming (SDP) relaxations for noncommutative polynomial optimisation, ubiquitous in quantum physics. However, its practical applicability is limited by the combinatorial growth in operator moments required at each level. Since not all moments contribute equally to bound tightness, selecting moments within a fixed computational budget is a relevant problem. We reframe moment selection as combinatorial subset selection and show it is governed by strong higher-order synergistic interactions among moments, quantified through a marginal synergy diagnostic adapted from complex systems theory. We develop and compare three optimisation methods: Parallel Tempering (PT), an RBM-based reinforcement learning policy, and Bayesian Optimisation (BO). On the $I_{3322}$ Bell inequality benchmark, all three substantially outperform greedy approaches at costs around two orders of magnitude below brute force, with the RBM achieving the closest approach to optimal throughout the hard transition regime. We apply the framework to the 174 Bell inequalities in the $(4,4,2,2)$ scenario, finding heterogeneous convergence behaviour across inequalities, and to the one-dimensional Heisenberg spin chain, demonstrating that physically motivated monomial bases are internally compressible and are not globally optimal in general. A budget-aware search over a broader pool improves certified bounds on long-range correlations by nearly two orders of magnitude. These results establish a scalable framework for moment selection in noncommutative polynomial optimisation, with broad applications across quantum physics and quantum information.

Quantum key distribution (QKD) can provide secret keys with security rooted in quantum mechanics, but operation alongside high-capacity classical traffic remains limited by the excess-noise budget of weak quantum states in conventional solid-core fiber. Here, we combine ultralow-loss anti-resonant hollow-core fiber with residual-carrier-assisted discrete-modulation continuous-variable QKD (DM-CV-QKD) to address both propagation-induced coexistence noise and low-SNR phase recovery. Over a 24.3-km hollow-core link with 3.3-dB end-to-end loss, a dual-polarization 15-Gbaud DM-CV-QKD channel achieves an average asymptotic secret-key rate (SKR) of 153.22 Mb/s and a finite-size SKR of 149.99 Mb/s, while 39 coherent wavelength-division-multiplexed channels deliver an aggregate data rate of 7.6 Tb/s and a net data rate of 7.2 Tb/s. The system can even sustain a positive SKR under a high classical launch power of up to 15 dBm, without an optical bandpass filter (BPF). Finite-size analysis against collective attacks further yields a projected positive secret-key rate at a 100-km-equivalent condition. These results show that an anti-resonant hollow-core fiber, combined with carrier-assisted phase recovery, can greatly extend the operating regime of shared-fiber quantum-secured coherent links, pointing to a promising approach for integrating high-rate CV-QKD with high-capacity optical networks.

We investigate interacting spinless electrons on finite molecular ring networks described by a tight-binding Hubbard Hamiltonian. The interplay between lattice geometry, Coulomb interactions and discrete symmetries is analysed for rings with $L=3,4,5,6$ nodes, filled with one, two or three electrons. Special attention is devoted to the role of the network symmetries in determining the structure of the many-body spectrum and the Mulliken classification of the eigenstates. Using group-theoretical methods, we examine the evolution of the spectra in the presence of an external magnetic flux. The Zeeman effect lifts degeneracies and results in combination with the Coulomb interaction to avoided crossings in symmetry sectors. We identify a qualitatively distinction between systems with an even and odd number of particles. At half-filling, particle-hole symmetry (duality) protects selected symmetry sectors against Zeeman splitting.

We numerically analyze a discretized version of the quantum contact process, a non-equilibrium model having a competition between a coherent infection process and a dissipative recovery process. Previously, the continuous-time version has been discussed to have a phase transition in a novel quantum universality class, while experimental investigations of a related process have pointed towards the transition being in the classical directed percolation class. We demonstrate that these differences stem from details of the discretization of the process and construct a systematic way to continuously interpolate between these results. Finally, we provide evidence that the modification of the universality class is due to destructive interference effects, showing a clear quantum-mechanical origin.

The Optimal Polynomial Intersection (OPI) problem asks us to find a low-degree polynomial over a finite field whose values lie in prescribed subsets on as many given inputs as possible. Decoded quantum interferometry (DQI) gives a quantum algorithm for OPI in parameter regimes beyond those achieved by the best known classical heuristics. Follow-up works improve the parameter regimes, but their analyses are limited to average-case settings. Recently, Sun and Wootters showed that, even in the worst case, OPI has a solution in a larger parameter regime than the one covered by DQI. However, they left open whether one can design a quantum algorithm that solves OPI in the worst-case beyond the DQI regime. We give such a quantum algorithm. As a byproduct, we also improve the existential bound of Sun and Wootters in certain parameter regimes. In particular, when each subset contains roughly half of the field elements, our algorithm finds a solution with satisfaction rate $s=1$ whenever the rate satisfies $R>0.75$. This matches the previous average-case bound, whereas DQI cannot achieve $s=1$ unless $R=1$. Our existential bound guarantees the existence of a solution when $R> 0.7158$, improving over the previous threshold $R>0.7495$. More generally, our existential results extend to the Max-LINSAT problem with respect to arbitrary maximum distance separable (MDS) codes. The corresponding algorithmic results apply only to MDS codes whose dual admits an efficient list decoder. Our results are obtained through a novel application of a Brascamp--Lieb-type inequality in the MDS setting, which may have further applications.

Logical Kochen-Specker (KS) contextuality is widely regarded as an intrinsic property of specially constructed measurement configurations. We show instead that it can emerge from KS-colorable vector sets through a constructive procedure we call the Hamilton extension. Defined for four-dimensional vector sets, the Hamilton extension associates each real vector with a measurement context while inducing additional measurement contexts among Hamilton-extended children of distinct parent vectors. These emergent contexts fundamentally alter the compatibility structure, transforming KS-colorable configurations into KS-uncolorable ones and recovering logical contextuality lost under apex-vertex augmentation. We establish a sharp and optimal threshold -- the Hamilton extension of every five-vector parent set remains KS-colorable, whereas suitably chosen six-vector parent sets already generate logical KS contradictions. Thus, six vectors constitute the smallest parent set capable of generating KS contradiction through this mechanism. Our results reveal a new structural route to contextuality, provide a systematic framework for constructing compact KS sets, and have implications for contextuality-based quantum information protocols and graph-theoretic approaches to nonclassicality.

Scalable quantum computing architectures increasingly rely on multi-core designs, where qubits are distributed across multiple processing cores interconnected through a quantum Network-on-Chip (NoC). In such systems, inter-core communication is typically realized through entanglement-assisted quantum teleportation, making efficient entanglement generation critical for performance. In this paper, we perform a comparative study of three entanglement management paradigms for multi-core quantum processors: reactive on-demand generation (ODG), proactive continuous pre-generation (CGP), and an adaptive continuous pre-generation approach (ACGP). While ODG generates entanglement only when required, CGP reduces average teleportation latency by pre-generating EPR pairs in the background. To improve upon this, we propose ACGP which dynamically adjusts entanglement generation probabilities based on observed inter-core communication patterns. We evaluate these approaches using an extended SeQUeNCe simulator on mesh-based multi-core architectures on real benchmark circuits. Results show that ACGP significantly reduces average teleportation latency compared to ODG and CGP. Although pre-generation introduces fidelity degradation due to storage time, entanglement purification effectively restores fidelity with minimal impact on latency. These results demonstrate that adaptive entanglement managements can substantially improve communication efficiency in scalable quantum multi-core systems.

No signaling in time (NSIT) has often been treated as the clean operational remnant of noninvasive measurability. If an earlier measurement leaves every later marginal unchanged, the temporal process appears classical. We show that this inference is false. We introduce common fixed points (CFPs) of nonselective measurement channels as an exact mechanism that erases all marginal evidence of invasiveness while preserving disturbed outcome conditioned branches. For a qutrit ring subject to a local degenerate Luders measurement, we solve the full CFP manifold analytically. Every state on this manifold satisfies exact pairwise NSIT, yet every nontrivial member violates a Leggett Garg inequality, including the maximally mixed state. The violation is governed by finite branch displacement, not by residual signaling. Hidden variable reconstruction, entropic witnesses, protocol landscape scans, noise robustness, and finite shot simulations show that exact NSIT certifies only the disappearance of marginal signals after outcome erasure, not the existence of a classical temporal history.

The Navascués--Pironio--Acín (NPA) hierarchy gives the standard semidefinite outer approximations to quantum behaviors. Whether \emph{any} finite level can already equal the quantum set has remained open even in the bipartite scenario with two binary measurements per party. We demonstrate that \emph{no finite level} is exact. For the symmetric doubly tilted CHSH functional $h_α=A_0B_0+A_0B_1+A_1B_0-A_1B_1+α(A_0+B_0)$, set $T=1-α$. Its quantum maximum satisfies $[ω_{\rm Q}(1-T)-(4-2T)]/T^3\to4/3$, whereas every fixed NPA level satisfies $[ω_L(1-T)-ω_{\rm Q}(1-T)]/T^3\to+\infty$. Under the corresponding boundary rescaling, an explicit expectation of the positive operator $ω_{\rm Q}(1-t^2)I-H_t$ converges to the Motzkin polynomial. A bounded fixed-level error would therefore make the Motzkin polynomial plus a nonnegative constant a sum of squares, which is impossible. Consequently, every standard NPA relaxation based on a fixed finite list of words in the measurement projectors strictly contains the complete quantum set, and its nonquantum behaviors accumulate at a local deterministic behavior. Thus, the finite-level exactness of CHSH and all one-sided tilted CHSH maxima does not extend to an exact finite-level description of the complete quantum set in the minimal scenario.

The quest for superconductivity created by light extends for more than half a century, yet direct evidence of a true zero-resistance state -whose macroscopic quantum phase coherence is both created and controlled by light -- has remained elusive. Here we report for the first time on a complex but robust light-programmable superconducting (LiPS) state at an aluminium-silicon heterojunction that is created and fully controlled with femtosecond laser pulses. The superconducting critical temperatures -- ranging from 1.8-8.5 K, can be increased or erased at will by the application of tailored pulse sequences. At low temperatures the LiPS state shows features characteristic of a Berezinski-Kosterlitz-Thouless topological transition, but another distinct state appears at temperatures above 2 K, which shows clear signatures of quantum phase disorder. In the presence of a magnetic field we observe behaviour characteristic of vortex pinning and creep consistent with the 2-dimensional (2D) nature of the phase coherent system. The origin of the LiPS effect is attributed to light pulse control of the Moire-like superlattice of misfit dislocations (MDs) that naturally occur as a result of discommensurations between Al and Si lattices at the interface, and is clearly observable by high-resolution electron microscopy. We show how light pulses can be used to control the superlattice periodicity and highlight the appearance of topologically protected soliton-like kinks along the dislocation lines, important for imparting metastability to the system. The demonstration of LiPS opens a route to the design of metastable long-range phase coherent superconducting states, leading to light-engineering of quantum circuits, local gap tuning in quantum processors and novel devices utilizing switchable superconductivity.

We investigate spin-resolved decay of an axion-like particle (ALP) into an electron--positron pair in an intense laser field. Using the Baier--Katkov quasiclassical operator formalism and the locally constant field approximation, we derive a compact analytic rate retaining finite ALP-mass effects and the lepton spin degrees of freedom. In the massless limit, the spin-summed rate has the same weak- and strong-field asymptotic scalings as the corresponding photon-induced pair-creation rate, while the pseudoscalar coupling induces distinct spin-resolved channels and spin correlations. A finite ALP mass reorganizes the spectrum across the vacuum threshold, producing purely field-induced pair creation below threshold and spin-dependent oscillatory modulations above threshold through the coherent interplay of vacuum and field-assisted contributions. The entanglement of the produced pair reflects the dominant production mechanism. Near the vacuum threshold in weak fields, the pair is nearly maximally entangled and singlet-like. Away from threshold, the reduced spin state becomes triplet-like, retaining a concurrence of \(1/2\) when strong-field production dominates but becoming separable when vacuum decay dominates. These results identify spin-resolved spectra and entanglement as signatures of finite-mass and threshold effects in strong-field ALP searches.

Basket option pricing often relies on Monte Carlo estimation, for which quantum amplitude estimation (QAE) provides a quadratic speed-up. However, the practical benefit of QAE can be limited by the depth of the state-preparation circuit. We propose a structure-aware quantum state-preparation framework for QAE-based basket option pricing. The framework uses tensor-train (TT) rank information to design shallow variational state-preparation circuits. In the independent regime, TT ranks remove unnecessary entangling links from a hardware-efficient ansatz. In correlated basket settings, we instead prepare asset-wise marginals locally and train a compact latent block to match the basket cumulative distribution function. The Basket-CDF objective targets the basket pushforward distribution rather than the full joint state, directly aligning state preparation with basket-dependent payoffs. Numerical experiments show that the proposed circuits replace the exponential state-preparation depth scaling of exact amplitude loading with linear scaling, while maintaining low-percent basket-pricing errors. Additional sampling-based training experiments and an end-to-end QAE integration study support compatibility with sample-estimated training and standard QAE-based pricing workflows.

The slowest Liouvillian mode often bottlenecks the relaxation of an open quantum system to its steady state. Standard strategies circumvent this bottleneck by selecting special initial states or engineering the dissipator. Here we show that neither is necessary. We introduce a pre-dissipative geometric steering protocol that reshapes any given pure or mixed state before relaxation begins -- coherent rotations interleaved with nonselective projective measurements -- at fixed Lindblad generator. By steering the state's Bloch direction along geodesic paths, the protocol suppresses its overlap with the slowest Liouvillian modes. The prepared state then starts farther from equilibrium yet relaxes faster, realizing the quantum Mpemba effect, whenever two computable conditions hold: reduced slow-mode overlap and a larger initial distance to stationarity. Our framework treats real and complex spectral gaps uniformly, and we demonstrate robust Mpemba acceleration in driven qubit and multiqubit systems using operations available in trapped-ion and superconducting platforms.

Scaling quantum networks beyond point-to-point links requires packet forwarding that can tolerate control-plane timing uncertainty. Existing optical-burst switching (OBS) and quantum-wrapper-based packet switching (QW) rely on external classical headers whose jitter-prone processing must remain aligned with in-flight quantum payloads, creating guard-window or fiber-delay-line budget failures. This paper proposes Syndrome-as-Header (SAH), a quantum label-switching architecture that embeds routing labels into the syndrome structure of an encoded payload. The proposed scheme uses Uncorrectable Error Injection (UEI) to map flow labels to reference syndromes and builds a syndrome-space header codebook whose decoding regions remain distinguishable under correctable channel-induced syndrome deviations. Core routers extract the syndrome header, suppress the correctable residual channel-error component, and swap labels without measuring or decoding the logical payload. SAH supports FAST forwarding and VERIFICATION mode, the latter providing end-to-end consistency checking while retaining swappable labels. In NSFNet timing benchmarks with common per-hop quantum error correction (QEC), SAH eliminates the external-header/payload alignment condition, so timing uncertainty appears as memory residence and delivered latency rather than alignment-budget drops. With a $T_2$-based memory-residence penalty, the advantage depends on router memory coherence; for sufficiently long coherence, SAH maintains higher acceptance and throughput than OBS+QEC and QW+QEC under high jitter.

Based on a photonic computing platform, we experimentally validate a generalized variational quantum linear solver (VQLS) by systematically solving four-dimensional linear equation systems across different fields. In the complex field, in addition to solving non-singular systems that admit a unique solution, we investigate ill-conditioned problems arising from singularity--an issue frequently encountered in practical applications. To tackle these challenges, we introduce perturbation terms, a treatment inspired by Tikhonov regularization, and develop an algorithm capable of handling a wide range of systems. Furthermore, we extend the VQLS to the finite field F2 by redesigning the cost function to incorporate modulo 2 and imposing several constraints on the solution vector. This modulo 2 VQLS is inherently free from singularity. It is adapt to stabilizer coding theory and may find applications in areas such as decoders and the design of quantum gate sequences. Therefore, our work demonstrates the practical potential of VQLS in quantum computing, providing a solid experimental foundation and methodological guidance for its real-world applications.

We construct binary Calderbank--Shor--Steane (CSS) quantum low-density parity-check (LDPC) codes from circulant permutation matrices (CPMs). The construction is parameterized by column weight J, row weight L, and prime lift size P. A J x J array of pair partitions imposes linear paired-difference equations on the CPM exponents. These equations give CSS orthogonality. The main finite examples reported here are the (J,L)=(4,12)-regular girth-six code [[372,130,16]] with rate 0.349, and the (J,L)=(4,14)-regular girth-six code [[518,228,16]] with rate 0.440. We also record (J,L)=(3,8)-regular girth-six instances [[472,122,14]] and [[488,126,14]], with lift sizes P=59 and P=61, respectively. The stated distances are established for the fixed matrices by exhaustive low-weight exclusion together with explicit non-stabilizer witnesses.

Strained membrane resonators have emerged as a promising platform for optomechanical accelerometry; however, the desired combination of low frequency and high $Q$-mass product requires a rethinking of their dissipation dilution engineering. Applying Bayesian optimization to a Si$_3$N$_4$ membrane, we discover a class of sail-like trampoline resonators in which the frequency is decreased by an order of magnitude while preserving the $Q$-mass product. We demonstrate centimeter-scale sails with kHz frequencies, $Q\sim10^7$ and $Q\times\text{mass}\sim$ 10 g. Vertically integrating a 7 kHz device with a nanoribbon, we realize a monolithic cavity optomechanical accelerometer with a room temperature thermal noise of $40\;\text{n}g_0/\sqrt{\text{Hz}}$, sufficient to resolve $μg_0/\sqrt{\text{Hz}}$ ambient vibration over a bandwidth of 4 kHz with a displacement imprecision of $10^{-14}\;\text{m}/\sqrt{\text{Hz}}$. Cryogenic arrays of sail membranes may be attractive for new physics searches and distributed quantum sensing experiments.

Hybrid continuous-discrete-variable quantum processors can represent bosonic degrees of freedom directly in oscillator modes, or qumodes, while using qubits for control, readout, and nonlinear operations. Recently proposed trigonometric continuous-variable (CV) gate sets promote periodic functions of oscillator quadratures to elementary operations, making them natural primitives for compact variables, rotor models, lattice gauge theories, and anharmonic dynamics. Here we experimentally demonstrate and benchmark one-qumode cosine gates, and perform a mode-resolved marginal benchmark of two-qumode cosine-gate implementations, on the QSCOUT trapped-ion quantum platform. Our implementation uses collective motional modes of three- and four-ion $^{171}{\rm Yb}^{+}$ chains and realizes finite-step trigonometric-gate circuits through hybrid qubit-qumode operations and conditional phase-space displacements. In contrast to previous theoretical and compilation work, we focus on the gate-level characterization of the trigonometric primitives. We measure Fock-space transition probabilities, study their dependence on gate parameters and Trotter step number, and compare with simulations incorporating thermal initialization and motional dephasing. We also derive ideal gate matrix elements and phase-space diagnostics, connecting the measurements to the non-Gaussian structure generated by these gates. These results establish trigonometric CV gates as reusable building blocks for bosonic Hamiltonian simulations and hybrid quantum algorithms requiring intrinsically non-polynomial operations.

Understanding the properties of defects is imperative for proper use for variety of applications including quantum computing. In this paper, we use the multiconfigurational self consistent field (MCSCF) combined with DFT optimized geometry in order to investigate the spin and optical properties of G centers in Silicon. By utilizing quantum chemistry based methods, we show excellent agreement with the Zero Phonon Line and Zero Field Splitting Tensor components of the G center. We also calculate the theoretical spin decoherence time of the G centers using Cluster Correlation Expansion (CCE) methods.

Large language models are increasingly assisting with demanding formal theorem-proving tasks, particularly when grounded in machine-checked libraries such as Lean. Agentic systems further amplify this process by searching, reusing, and extending existing formal developments to uncover new discoveries. In quantum computing, Shor's algorithm and its variants present such a demanding case for Lean formalization. In this work, we formalize this algorithm family in Lean through agentic formalization: software agents analyze sources, write Lean code and repair proofs, with human review of the scientific claims and machine checking of the resulting formal proofs. Our formalization develops the mathematical foundations for analyzing quantum attacks in two cryptographic settings: a 2048-bit modulus in the RSA-2048 and the standardized elliptic curve over a 256-bit prime field (P-256). To support these analyses, the formalization ranges from quantum algorithms for order finding to reversible quantum circuits for modular and elliptic-curve arithmetic. Based on [Quantum 5, 433] and [ASIACRYPT 2017, 241--270], we formalize the logical resource estimates for RSA-2048 and P-256, respectively, and provide additional estimates of classical operations. We expect the results pave the way for broader machine-checked quantum cryptanalysis and represent a step toward AI-assisted design and verification of quantum algorithms.

Quantum annealing is often limited by population trapped in local minima many spin flips from the solution. We introduce a mathematical framework to understand the connection between energy and Hamming distance in optimization problems. Using this, we build ZZ-catalysts from ground-state patterns of small frustration-free subproblems that make configurations far from the solution less energetically competitive. On sparse problems they multiply the near-solution probability at short sweeps, with gains persisting on fully-connected models and tunable via subproblem choice.

The monogamy of quantum entanglement, applied by Almheiri-Marolf-Polchinski-Sully (AMPS) to black holes, obstructs a smooth horizon vacuum after the Page time. We transcribe this argument to Hawking-like phonon radiation from a sonic horizon in the Unruh acoustic metric. An exact purity identity shows that post-Page-time unitarity forces the entanglement between an outgoing phonon and its interior partner to vanish, selecting a non-Hadamard (Boulware-like) phonon state, which we define as an acoustic firewall. Its renormalized stress tensor differs from the smooth state by a constant, negative near-horizon flux, and the thermal-atmosphere energy density it removes, measured by a static calorimeter, grows as $(δr)^{-2}$ in the radial coordinate toward the horizon (singular in the free-fall frame), cut off at the healing length. The construction is kinematic and does not resolve the information paradox; it yields one concrete, falsifiable prediction: a differential phonon-calorimetry signal $\mathcal{R}(δr)=|Δ\mathcal{E}|/\mathcal{E}^{(0)}\to(\ell_κ/δr)^{2}$, present only after the analogue Page time in a Bose-Einstein condensate.

We formulate timelike entanglement entropy and its Rényi extension in two-dimensional conformal field theory through the analytic continuation of replica twist correlators to time-ordered, timelike-separated insertions. This field-theoretic construction grounds and generalizes recent developments, and applies to temporal subregions of arbitrary extent. Within three-dimensional holography, the semiclassical boundary correlator identifies boundary-anchored complex geodesics as the relevant bulk saddles and selects the one with the smallest real part of the length. This provides a direct boundary derivation of the proposed complex extremal surface prescription and extends to Rényi index $n>1$, for which we explicitly construct the corresponding complex cosmic brane geometry in the vacuum. We develop these ideas in several representative settings, including locally and globally excited states and quantum operator quenches, making manifest the precise agreement between boundary twist correlator and bulk complex geodesic calculations. For AdS-Vaidya, our approach predicts a different result from earlier piecewise geodesic constructions, while reproducing the field theory answer. Across these examples, the operator ordering uniquely determines the imaginary part of the complex-valued entropy, which is quantized in units of $cπ/6$ and sensitive to the effective causal structure but not to the underlying dynamics.

Driven by recent discoveries regarding the quantum Mpemba effect, the anomalous relaxation dynamics of open quantum systems have garnered significant attention. While expediting thermalization to equilibrium has been extensively studied, dynamically accelerating the convergence toward nonequilibrium steady states remains a formidable challenge. In this article, we find a transient engineered nonreciprocal dissipative channel can provide a shortcut that accelerates convergence to the target reciprocal nonequilibrium steady state for the considered two-mode model and initial states. Using interacting bosonic modes, we demonstrate that the temporal activation of a nonreciprocal channel efficiently suppresses prolonged inter-mode energy oscillations, enforcing a rapid, unidirectional thermal dump into the environment. Counterintuitively, we find that this relaxation speedup is robust and independent of the direction of the nonreciprocity. Our results provide a powerful thermodynamic technique for rapid state preparation and cooling in continuous-variable quantum systems, particularly critical for low-temperature quantum information processing.

Schrödinger cat states provide a hardware-efficient platform for bosonic quantum error correction by encoding logical information in protected manifolds of harmonic oscillators. While previous work has demonstrated the dissipative stabilization of multi-mode Schrödinger cat states as robust quantum memories, a framework for universal quantum computation has remained unavailable. Here we extend this approach by introducing a universal gate set for dissipatively stabilized multi-mode cat qubits. Using a chain of Kerr non-linear oscillators coupled through engineered non-local dissipation and an effective low-dimensional description, we show how arbitrary single-qubit control can be achieved through arbitrary rotation around the $X$-axis and $π/2$-rotation around the $Z$-axis. We further show how coupling two such stabilized arrays through just one oscillator on each respective array enables coherent entangling operations through implementation of the $XX(π/2)$ gate. Numerical simulations demonstrate high-fidelity gate dynamics and entanglement generation under realistic parameters. Finally, we analyze the effects of induced and intrinsic photon loss, disorder, and the validity regime of the effective low-dimensional theory. Our results establish dissipatively stabilized multi-mode Schrödinger cat states as a potential architecture for universal bosonic quantum computation.

Spectral measurements provide fundamental insights into wave systems by revealing resonances, mode hybridization, and light-matter interactions. However, intrinsic dissipation and measurement-induced spectral broadening often conceal the underlying hybridized light-matter states that give rise to measured spectra. Here, we establish a space-time Fourier correspondence that interprets spectral broadening as an effective temporal attenuation, giving rise to a temporal Fourier optics framework for recovering hidden spectral information. Implemented through a temporal point-spread-function (TPSF) reconstruction method, the framework compensates the effective temporal decay before Fourier transformation, directly reconstructing intrinsic spectral responses from experimentally measured spectra without repeated frequency synthesis or model-dependent fitting. We experimentally validate the approach in deterministic single-molecule Au nanosphere dimers and open Au@Ag nanorod- and nanotriangle-based plasmonic nanocavities coupled to J-aggregate excitons. Across these diverse platforms, TPSF consistently reconstructs hidden upper and lower polaritonic branches, thereby revealing the underlying hybridized light-matter states and strong coupling that remain unresolved in conventional scattering spectra. The reconstructed spectra agree closely with the recently developed complex-frequency formalism while offering a considerably simpler and more experimentally accessible implementation. Beyond strong light-matter coupling, temporal Fourier optics establishes a general framework for uncovering physical states hidden by dissipation, opening new opportunities for spectroscopy, imaging, sensing, and inverse wave measurements across photonics and wave physics.

The classical observables of position and momentum are not well adapted to particles in a microphysical situation where typical probability distributions are characterized by a substantial dispersion. We propose the use of more robust quantum observables for probabilistic classical particles. The quantum observables are statistical observables which do not take fixed values for a given classical position and momentum. Solutions of the Liouville equation are discussed in the quantum formalism for classical statistics. Statistical observables are represented by non-commuting operators. No classical correlation function is defined for these observables and Bell's inequalities do not apply. We demonstrate for a general potential how a quantum system emerges from classical statistics. For the particular cases of a harmonic potential and a Coulomb potential we investigate subsystems which describe all features of a quantum particle. This covers the discrete energy spectrum of the hydrogen atom and quantum harmonic oscillator. We discuss the interference for the double-slit experiment. Conserved statistical observables may also be relevant for the probabilistic dynamics of dust or planets.

Quantum gate synthesis is essential for implementing quantum algorithms on real hardware, yet existing methods are often computationally demanding. Here, we introduce a novel approach based on reservoir computing, which we name Group Reservoir Computing, an efficient machine-learning paradigm for learning temporal dynamics whose training reduces to a single linear regression, to reduce the resources required. The method is grounded in the Wei--Norman decomposition, which provides a compact description of the evolution. We prove that the reconstructed dynamics always remain unitary by construction and derive formal error bounds that establish the theoretical validity of the strategy. On the standard single-qubit gate set the trained network produces a control pulse in a single pass, with mean fidelity 0.94 across the eight benchmark gates; used to warm-start gradient-based optimization, it roughly halves the number of iterations that plain gradient ascent needs to reach a target fidelity, so that the relevant figure of merit is the time to reach that threshold rather than the final accuracy after a fixed budget. Owing to its general formulation, the method applies to any finite-dimensional hardware platform; the route to multiqubit synthesis is discussed in the closing section.

The free-particle and square-well potentials are two of the most emblematic problems in quantum mechanics, illustrating essential concepts such as matter waves, energy quantization, and bound states. It is therefore natural to consider how the free-particle solutions emerge from the square well as the width approaches infinity. In this work, we present a systematic procedure to demonstrate this transition by applying a Fourier transform to the wave equation.

Thanks to low dissipation, superconducting devices are promising for a number of applications, such as detectors and implementations of quantum computation. However, their working can be adversely impacted by quasiparticles, which is why so-called quasiparticle poisoning mechanisms and their mitigation are under intense investigation. Here we focus on one poisoning mechanism, namely pair-breaking phonons, and its mitigation through down-conversion by a normal-metal film - the process in which scattering of high-energy phonons by electrons lowers the energy of the former below the pair-breaking threshold. To study the down-conversion, we introduce a model based on kinetic equations, which we solve both analytically (approximately) and numerically in the steady state. We use the solution the estimate a properly-defined down-conversion efficiency which depends on material parameters (such as the strength of electron-phonon interaction and the phonon transmission coefficient at interfaces) and film and substrate thicknesses. Interestingly, we find that the efficiency is nearly optimal over a finite range of metal thicknesses, with the minimum near-optimal thickness being typically of the order of a micron.

In this paper, we study the non-Markovian quantum dynamics in quantum computations from the perspective of a Lie algebraic approach based on numerical analysis. By vectorizing the density matrix of quantum states, the non-Markovian evolutions can be represented with high-dimensional linear time-varying equations, where the time-varying parameters arise from the non-Markovian interactions between the quantum system and environment. We study the Magnus expansion of such linear time-varying quantum dynamics and clarify how the truncation errors for the first- and second-order Magnus expansions are influenced by the non-Markovian properties. Besides, when the quantum states are measured for filtering, the dynamics can be modeled as time-varying stochastic differential equations due to the existence of measurement noise. The Magnus expansions based on quantum stochastic filtering are different when the quantum measurement noises are modeled in an {Itô} or Stratonovich approach, rendering different truncation errors. Based on this, numerical simulations further demonstrate the efficiency of Magnus expansions in simulating non-Markovian quantum dynamics without or with stochasticity, and how the truncation errors are influenced by the Lie algebras in the Liouville space.

Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high-dimensional data in Hilbert spaces, but its practical success depends critically on how classical data is encoded into quantum states. We introduce \emph{quantum topological data encoding} (QTDE), a general framework for encoding topological information into quantum states via topology-driven quantum evolution. Our method generalises an existing topology-driven quantum encoding framework to higher-dimensional data. We test the proposed method on clique-complexes classification tasks, and provide preliminary evidence that topology-driven quantum representations can capture discriminative information beyond that available through direct comparisons of classical topological descriptors. The proposed quantum representations consistently outperform a baseline based on direct comparisons of the combinatorial Laplacians describing the underlying topological structure. We indicate several areas of application where the framework can be used to provide a more efficient and reliable data representation.

Quantum phase transitions provide a controlled route for generating many-body excitations, but the dynamics after the critical point can be as important as the initial defect creation. Recent progress in quantum annealing has made it possible to access coherent nonequilibrium dynamics in programmable Ising systems with thousands of superconducting qubits. Here we use this capability to study a longitudinally biased quantum Ising chain, where Kibble--Zurek defect creation is followed by nonintegrable post-critical dynamics. The longitudinal bias confines kink--antikink excitations into mesonic bound states, so that the final spin configurations encode both the production of defects near the critical point and the subsequent evolution of the confined excitations. Using energy-scale rescaling and zero-noise extrapolation, we find that the defect density follows the expected biased Kibble--Zurek/Landau--Zener crossover and agrees with matrix-product-state simulations with uniform bias. In contrast, magnetization, spatial profiles, and minority-domain statistics reveal that mesonic evolution is interrupted by localization of the post-critical domain pattern. Matrix-product-state simulations with disorder reproduce this separation between robust defect creation and localized post-critical dynamics. Our results show that large-scale quantum annealers can probe the fate of critical excitations beyond defect counting.

Scalable quantum computing is limited by the dense network of electrical interconnects linking cryogenic quantum processors to room-temperature control electronics. To overcome this bottleneck, considerable effort has focused on cryogenic CMOS electronics and microwave-to-optical transduction, aiming to reduce wiring complexity and thermal loading. Wireless interconnects have recently emerged as a promising complementary approach, yet their compatibility with superconducting quantum hardware remains largely unexplored. Here, we demonstrate the wireless excitation of a superconducting microwave resonator of the type routinely employed for qubit readout, operating at millikelvin temperatures inside a dilution refrigerator. By directly comparing wired and wireless operation within the same cryogenic environment, we show that wireless coupling preserves the intrinsic resonator response while revealing parasitic electromagnetic pathways arising from stray radiation within the cryostat enclosure. These results establish a framework for the co-design of wireless interconnects, cryogenic packaging and superconducting quantum hardware.

The field of quantum machine learning (QML) evolved to value models believed to most directly rival those providing utility in classical ML, namely large-scale neural networks. Although more recently, classical ML has been learning a hard lesson with respect to deploying un-interpretable neural networks in the wild: model interpretability matters for domain-adapted co-design and human adoption. We adopt this larger ML perspective to argue that quantum ML model value can be found through the characterization of its inherent interpretability offerings -- i.e. its mathematical structure that contributes meaningfully to desired model behavior for the specific ML task. To support our perspective, we provide a motivating example of a characterization with quantum Fourier models and random Fourier features (RFF) as approaches to approximate Gaussian process (GP) kernels for uncertainty quantification tasks in ML. The top-down and bottom-up complementarity of the two mathematical constructions reveals that quantum Fourier models offer different tools than RFFs for principled GP kernel design and interpretable discovery for uncertainty quantification with real-world data. To showcase the rich variety of inductive biases enabled by quantum information tools, we review examples from the QML literature -- including symmetry, metric geometry, and topology -- that can be used to design inherently interpretable ML models for specific tasks. We hope this framing encourages the QML community to value the inherent components and mechanisms of quantum models separately from task performance, as inherent interpretability might be the reason that a quantum model, and potentially a quantum computer, gets used in practice for ML.

The Elliptic Curve Discrete Logarithm Problem (ECDLP) is a fundamental problem in cryptography, and reducing the resource requirements of quantum algorithms for solving ECDLP is an important goal. In this work, we present a space-efficient quantum algorithm for solving the ECDLP over prime fields, achieving an implementation with only $3n+6\lfloor \log_2 n \rfloor+O(1)$ logical qubits and $919n^3/\log_2 n+O(n^2)$ Toffoli gates, where $n$ is the bit-length of the prime. For a 256-bit prime-field curve, our construction requires only 835 logical qubits, reducing the previous best estimates of 1098 and 1175 logical qubits by Chevignard et al. [EUROCRYPT 2026] and Babbush et al. [ArXiv Preprint 2026], respectively. The key to our improvement is a new space-efficient reversible modular inversion circuit, which addresses the dominant space bottleneck in affine-coordinate point addition. Starting from the extended Euclidean algorithm (EEA), we refine the register-sharing technique of Proos and Zalka by introducing length registers and location-controlled arithmetic to compactly store and update intermediate variables. We further optimize the reversible update procedures and construct the corresponding controlled arithmetic circuits, resulting in a modular inversion circuit implemented by only $2n+6\lfloor \log_2 n \rfloor+O(1)$ logical qubits and $195n^2+O(n\log_2 n)$ Toffoli gates. This modular inversion circuit together with mid-circuit measurements and classical feed-forward operations provides a space-efficient controlled affine point-addition circuit and a complete implementation of Shor's algorithm for ECDLP.

The Markovian dynamics of open quantum many-body systems are typically governed by the Lindblad master equation, yet obtaining the reduced density matrix for bosonic and fermionic systems remains a formidable numerical challenge due to the exponential growth of the Hilbert space. Here, we introduce a computationally efficient framework for constructing the reduced density matrix by modeling the environment as a copy of the primary system with a monotonically decaying coupling that enforces unidirectional energy flow. Our method directly yields exact solutions that rigorously satisfy the Lindblad master equation for both bosonic and fermionic cases, bypassing the need for costly Liouvillian diagonalization. We validate our approach through applications to paradigmatic models, demonstrating accurate reproduction of dissipative dynamics across a broad parameter regime. This work provides a straightforward and powerful tool for simulating Markovian open-system evolution, with immediate applicability to quantum transport and control problems in many-body physics.

In this work, we introduce a complementary optimization method for progressive and adaptive state search (COMPASS) based on biorthogonal adaptive recurrent neural quantum states. Our approach combines an adaptive autoregressive architecture with a biorthogonal variational Monte Carlo scheme as well as a complementary optimization scheme that alternates between energy and variance minimization. This enables the stable convergence to ground-state eigenpairs, while avoiding Markov chain sampling through exact autoregressive generation. We demonstrate that for parity-time(PT)-symmetric Hamiltonians, unconstrained complex ansatze can spontaneously break PT symmetry during optimization, even in the unbroken phase, leading to spurious imaginary energies. Real-valued ansatze, on the other hand, naturally constrain the optimization to the correct physical manifold. Conversely, for generic non-Hermitian (NH) Hamiltonians without symmetry protection and complex spectra, complex ansatze are essential for capturing complex ground-state properties. Our results establish that physically-informed ansatz selection is crucial for reliable NH simulations. By combining adaptive architectures, biorthogonal optimization, and symmetry-aware modeling, this framework enables a direct study of 1D and 2D NH many-body systems without Hermitian embeddings or adiabatic continuation. Applying this framework to systems with frustrated magnetism, we show that gap frustration provides a quantitative shield against NH spectral instability, with the frustration gap setting a critical threshold for PT-symmetry breaking. Also, complexifying the frustration coupling itself generates a new topologically nontrivial network of diabolic level crossings, controlled by the phase of the complex coupling, that has no Hermitian analog. We term this novel spectral topology in NH frustrated systems the diabolic ring.

We present a framework for foliating any Pauli-based quantum error-correcting code over prime-dimensional qudits. For any such code, we obtain a qudit graph state that can be measured to perform fault-tolerant measurement-based quantum computing. Such a paradigm is of interest in platforms such as photonics, where measurement-based protocols are natural and high-dimensional states are readily available. We discuss several examples for arbitrary prime dimension $d$, such as the qudit toric code (stabilizer, CSS), the $d$-dimensional perfect $[[5,1,3]]$ code (stabilizer, non-CSS), and a straightforward $d$-dimensional generalization of the CSS honeycomb code (dynamical, CSS). Under a simple error model, we numerically calculate thresholds for the foliated qudit toric code and demonstrate that they are comparable to the non-foliated version.

Gigena et al. [npj Quantum Inf. 11, 82 (2025)] proved the exact quantum maximum of the doubly-tilted CHSH functional $B_{αβ}=α\langle A_0\rangle+β\langle B_0\rangle+\mathrm{CHSH}$ and observed that the NPA level required to reach it grows without evident bound toward the critical line $α+β=2$. We quantify the mechanism on the symmetric slice $s=2-α-β$: (i) the quantum value leaves the local bound cubically, $c_Q=4-s+s^3/6-s^4/36+O(s^5)$; (ii) each NPA level overshoots quadratically, $c_k(s)=4-s+a_k s^2+O(s^3)$, with the almost-quantum coefficient computed exactly, $a_{1+AB}=3/64$; (iii) the divergence of the required exact level is equivalent to positivity of the single sequence $(a_k)$ - proven for every $k$ in the companion paper. We prove the supercritical side completely: for all $α,β\ge 1$ and every level the hierarchy is exact, via three explicit rational certificates realizing an affine identity. The hierarchy's exactness thus undergoes a phase transition at the critical line. On the subcritical side we certify the first four levels in exact arithmetic (rational pseudo-moments beating $c_Q$, confirmed by Sturm's theorem). We identify the exact mechanism: rescaled to the critical corner, the limiting obstruction is the Motzkin polynomial, the classical nonnegative-but-not-sum-of-squares form, so the finite-level failure sits in the restricted-certificate regime. The phase boundary has a precise geometric reading via Nie's finite-convergence theorem and Marshall's boundary Hessian condition: a self-tested optimum is finitely NPA-certifiable whenever its boundary Hessian is nondegenerate (contact order two), which holds for the single tilt and fails exactly at the doubly-tilted cubic touch. Three verified errata in the published polynomial system of Gigena et al. are documented.

Gigena, Panwar, Scala, Araujo, Farkas and Chaturvedi [npj Quantum Inf. 11, 82 (2025)] determined the quantum maximum of the doubly-tilted CHSH functionals, observed that the Navascues-Pironio-Acin level needed for exactness grows without evident bound toward the critical tilt, and asked whether any finite level suffices. We answer this in the negative. For the symmetric critical family $B_s=(1-s/2)(\langle A_0\rangle+\langle B_0\rangle)+\mathrm{CHSH}$ we prove: for every NPA level $k\ge 2$ there are an explicit rational $g_k>0$ and an $s^*_k>0$ with $c_k(s)\ge 4-s+g_k s^2$ on $(0,s^*_k]$; since the quantum value leaves the local bound only cubically, every finite level strictly overshoots on an interval: no finite level is exact on any neighbourhood of the critical point. Unconditionally $a_2>1/39$, $a_3>1/188$, $a_4>1/641$. The proof is a primal construction: an exactly feasible moment curve at each level, built from level-uniform structural laws and one level-independent signed witness -- a closed-form class function $y^*$ with $N_k^TΓ(y^*)N_k=u_k u_k^T$ at every level. The mechanism forces the sign: for $k\ge 3$ no quantum state and no smooth curve of quantum models can realize the gain direction, so the overshoot lives strictly in the non-quantum part of the NPA tangent cone. The proof is computer-assisted in the strict sense: finite exact-integer verifications with proven degree bounds are constituent parts of the argument; the chain has been re-verified against independent implementations, including a symbolic per-regime proof of the witness identity and a clean-room implementation written from the paper text alone. The one external input is the published quantum value of Gigena et al., cross-checked to twelve digits.

In this work, we consider the problem of computing a linear combination over a noiseless quantum many-to-one network. There are $k$ senders, Alice$_1$, $\ldots$, Alice$_k$, and a single receiver, Bob. Each Alice$_i$ has a data vector $W_i \in \mathbb{F}^{m_i}$, where $\mathbb{F}$ is a finite field. Bob wants to compute the linear combination $Y = V_1 W_1 + V_2 W_2 + \cdots + V_k W_k \in \mathbb{F}^m$, where $V_i$ is an $m \times m_i$ matrix over $\mathbb{F}$. The senders transmit quantum states to Bob through a noiseless many-to-one quantum network, but they are not allowed to communicate with each other. The senders share entanglement among themselves, while Bob does not share this entanglement. They encode their classical information $W_i$, $i=1,\ldots,k$, into their local subsystems and transmit them to Bob so that he can recover $Y$ through a quantum measurement and subsequent post-processing. The N-Sum Box protocol proposed by Allaix et al. (2025) considers this problem under certain constraints on the linear combination and the distribution of the data vectors among the senders. We present protocols that support the computation of a more general class of linear transformations by giving the senders access to more qudits and allowing them to judiciously precode their input symbols. The communication cost of our schemes is at most that of the best-known prior results in this area and is strictly lower in certain cases. Finally, we demonstrate that the communication cost is subadditive across instances. Specifically, we identify two linear functions for which the total cost of computing them individually is strictly larger than the cost of computing them jointly.

Matrix models appear as fundamental descriptions of M-theory and D-brane dynamics, and via the gauge/gravity duality their gauge-invariant, or singlet, sector describes the purely gravitational degrees of freedom in the holographic dual. We present a new exact algorithm for computing observables of bosonic U(N) matrix models in the gauge-invariant singlet sector. This sector is spanned by an orthogonal basis of Schur polynomials (for a single matrix) and restricted Schur polynomials (for multiple matrices), which diagonalizes the free Hamiltonian and provides a natural truncation of the Hilbert space by excitation number. Matrix elements of the interaction Hamiltonian, or any gauge-invariant observable, are evaluated through a group-theoretic reduction to cosets and double cosets of suitable subgroups of the symmetric group, together with character sums on the symmetric group. The resulting entries are closed-form polynomials in the gauge-group rank N, assembled from group-theoretic data that are precomputed once and can be reused for any N and any coupling constants. We validate the one-matrix implementation against the exact mapping to N non-interacting fermions, demonstrating rapid convergence of the low-lying spectrum with the cutoff. The multi-matrix extension is outlined; its main bottleneck is the computation of restricted characters of the symmetric group, for which no algorithm comparable to the Murnaghan--Nakayama rule is currently known. The framework gives direct access to finite-N, finite-coupling dynamics of gauge-invariant states and opens a new computational window on the non-planar regime of holographic matrix models.

The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers. However, even though current quantum computers are limited in terms of size and precision, they can still be useful for finding loopholes and weaknesses in the post-quantum cryptographic protocols. In this work, we present an attempt to utilize the capabilities of Quantum Generative Adversarial Networks (QGANs), one of the promising architectures used in quantum machine learning, for this purpose. We describe an example application of QGAN architecture for the purpose of loading the probability distribution of the hash-based digital signatures into the memory of a quantum computer. Our results confirm that near-term hybrid quantum-classical methods possess capabilities required for this purpose. The presented approach can be used as a first step in the workflow, enabling the utilization of quantum computing for attacking post-quantum cryptographic primitives.

This thesis investigates the application of machine-learning methods in the context of quantum computing and neutrino physics, with particular emphasis on the construction of effective representations for complex, high-dimensional data. The first part of the work is devoted to Quantum Extreme Learning Machines (QELMs), a hybrid quantum--classical framework in which classical data are encoded into quantum states and processed through fixed quantum dynamics, while learning is performed by a classical readout layer. Within this framework, we analyze the role of encoding strategies, feature-reduction methods, Hamiltonian structure, and measurement, with particular focus on the relationship between quantum dynamics, expressivity, entanglement, and classical simulability. The second part of the thesis concerns the application of deep learning to the analysis of images produced by water Cherenkov detectors in neutrino physics. Convolutional architectures, including residual networks, are developed for the classification of complex events in realistic simulated datasets, showing that such models can effectively extract relevant information from detector data. Taken together, these results highlight the potential of machine learning, in both its classical and quantum forms, as a powerful framework for the analysis of complex data in fundamental physics, while also outlining relevant challenges and directions for future research.

Nucleation of vortex-antivortex pairs (VAPs) is believed to play a central role in the photon detection mechanism of superconducting detectors; however, their direct dynamic observation has remained challenging. Here, we report the direct observation of photon-induced VAP dynamics in a current-carrying superconductor as quantized voltage signals following photon absorption. The observed signals are interpreted as discrete phase-slip events, where each vortex traversal induces a 2-pi phase change of the superconducting order parameter, resulting in a quantized voltage pulse whose time integral is given by the magnetic flux quantum. We analyze the resulting quantized signals as a function of bias current, base temperature, and input photon-number states, and find that the number of VAPs generated per absorbed photon becomes effectively stabilized under specific conditions. Under these conditions, we demonstrate photon-number-resolving capability by directly counting phase-slip-induced voltage quanta. Our results reveal a detection mechanism governed by phase dynamics rather than conventional resistive transitions. We further show that photon-number resolution emerges when the fluctuation of photon-induced vortex-antivortex pair generation becomes statistically suppressed. These findings establish a new route toward photon-number-resolving detection based on phase-slip counting and open opportunities for high-speed superconducting detectors for quantum optics and photonic quantum technologies.

Multipartite Bell nonlocality is a central resource for device-independent quantum information protocols, but its practical certification is inevitably affected by imperfect measurements. We analyze how coherent angular misalignment and incoherent outcome flipping affect Bell-value degradation and nonlocality thresholds in $n$-partite GHZ states based on the Mermin, Svetlichny, and Mermin--Ardehali--Belinskii--Klyshko (MABK) inequalities. Coherent misalignment produces periodic angular violation windows whose individual widths shrink with the number of parties. In contrast, incoherent outcome flipping yields a single critical outcome-flipping probability, which increases with $n$ for MABK and the odd-$n$ Mermin inequalities, but decreases with $n$ for the Svetlichny inequality. Connecting the degraded Bell values to asymptotic Devetak--Winter key-rate bounds under a convex-combination attack model shows that secret-key generation imposes stricter constraints on measurement imperfections than nonlocality certification. These results provide quantitative benchmarks for robust multipartite nonlocality certification and key-rate estimation under measurement imperfections.

Particle Image Velocimetry (PIV) is the prime image-processing technique to measure and visualize velocity fields of laminar and turbulent flows. The velocity field vectors are obtained with sub-pixelaccuracy by analyzing cross-correlations, empowered by Fast Fourier Transforms (FFT). Here, we present a quantum algorithm with multidimensional quantum Fourier Transforms, termed Quantum-based PIV (QuPIV), to replace the classical computation of up to millions of velocity vectors. Our end-to-end quantum algorithm includes a novel state preparation, modified amplitude amplification, and the output extraction. We enhance amplitude amplification by a contracted ground-state projector, which allows a significant reduction of the number of gates in the quantum circuit. We justify the end-to-end capability with numerical studies on all stages of the algorithm on both synthetic and experimental data.

We study the separation of geometric effects from quantum interference in quantum optimization algorithms. Constrained optimization problems such as routing, assignment, and scheduling are often encoded as product spaces of local variables, together with global feasibility penalties. The central algorithmic question we address is how a constraint-preserving mixing operator transports quantum amplitude across an exponential search space in the presence of local and global constraints. We develop a framework that separates three effects that are usually intermixed: amplitude transport, coherent interference among transported amplitudes, and problem-dependent classical postprocessing. We show that the mixing operator alone does not have a target-seeking ability. Concretely, the normalized distribution induced by its amplitude transport moves toward the distance profile of a uniformly random configuration. Thus, quantum sampling advantage may only arise when the phases of the many computational paths reaching a target configuration are sufficiently aligned for their amplitudes to reinforce. We show that, when the cost phases are engineered so that these paths add coherently, a number of circuit alternations growing only logarithmically with problem size suffices to convert the sum of their absolute contributions into a lower bound on the target amplitude, yielding a certified success probability independent of the ambient Hilbert-space dimension, the search-space size, or the feasible-set cardinality. We develop applications to problem-specific transpilation diagnostics, scalable hardware probes, constraint-induced classical maps of quantum-generated samples, the attribution of solution quality between the quantum distribution and classical post-processing in hybrid quantum-classical workflows and connections to distance-partitioned product spaces from classical coding theory.

Fast and accurate classical simulation of quantum systems is a central challenge in the design and control of quantum computers, but the highly oscillatory dynamics of these systems severely limit the efficiency of standard numerical methods. To address this, we adapt Filon quadrature for oscillatory integrals into two numerical methods, called Filon and Controlled Filon, for solving linear systems of ODEs with highly oscillatory solutions. We tailor both methods for efficient implementation in controlled quantum systems, and the Controlled Filon method additionally accounts for the oscillatory structure of the control pulses. We show by numerical experiments that these methods significantly reduce the computational cost of accurately simulating systems of superconducting transmon qubits by decreasing the number of timesteps needed to reach a given level of precision, with only a modest increase in the cost per timestep. For a realistic simulation of the dynamics of a CNOT gate, the Controlled Filon method is the most efficient method tested at every target accuracy, outperforming the best Hermite method by up to 6x and the Hermite method of the same order by up to 500x.

The maximum likelihood decoder based on tensor networks has proven highly successful for the 2D surface code, achieving the optimal decoding success rate. However, existing tensor network decoders are typically designed for independent single-qubit error models, and their performance under inter-qubit correlated error models remains unexplored. This is due to two major challenges. The first challenge lies in constructing the tensor network for correlated errors, since the same final Pauli error can arise from many different combinations of independent and correlated errors, preventing a direct factorization of the error probability. The second challenge is that even after a tensor network is constructed, it generally contains huge-dimensional tensors and is therefore not efficiently contractible. In this work, to address the first difficulty, we introduce additional binary indices and two transformations to construct a multi-index tensor network for maximum-likelihood decoding with correlated errors. To address the second difficulty, we use reparametrization, elimination, and index classification to decompose the huge-dimensional tensors into lower-dimensional tensors. This yields an efficiently contractible tensor network for error models satisfying the tractability conditions derived in this work. We perform numerical simulations for a representative correlated error model and show that the maximum-likelihood decoder implemented with our multi-index tensor network construction achieves a higher finite-size threshold than the widely used MWPM decoder.

Quantum random number generators are essential for achieving information-theoretical security in modern cryptographic systems. Among various implementations, laser phase noise schemes are widely favored for their simple architecture and high integration potential. However, the intrinsic correlations in the raw data are often neglected, which violates the independent and identically distributed assumption and potentially compromises system security. In this work, we establish an analytical model of correlation and formulate an analytical expression for the conditional min-entropy in the presence of intrinsic correlations to accurately quantify the genuinely extractable randomness. The validity of our theoretical model is confirmed by numerical simulations and experimental results, exhibiting excellent agreement. Under typical setups it is shown that neglecting intrinsic correlations leads to an overestimation of extractable randomness by approximately 46%. This work provides a valuable theoretical framework for designing compact, high-performance quantum random number generators with rigorous security analysis.

Quantum coherence is a fundamental quantum resource whose preservation under environmental interactions is essential for quantum information processing. While most studies have focused on basis-dependent coherence measures, the dynamics of intrinsic coherence quantified by basis-independent measures remain largely unexplored. In this work, we investigate the dynamics of basis-independent quantum coherence for several representative tripartite pure and mixed states subjected to local and common dephasing environments in both Markovian and non-Markovian regimes. We show that Markovian local dephasing leads to state-dependent coherence degradation, whereas collective dephasing significantly enhances coherence preservation through decoherence-free sectors. More importantly, non-Markovian environments give rise to nearly frozen coherence dynamics for both pure and mixed states, demonstrating the remarkable robustness of intrinsic coherence against dephasing environment. A comparison with the measure of relative entropy of coherence reveals that basis-independent coherence measure exhibits substantially greater resilience and qualitatively different dynamical behaviour than its basis-dependent counterpart. These results provide new insights into the preservation of intrinsic multipartite coherence in open quantum systems and highlight basis-independent coherence as a robust quantum resource for realistic noisy quantum technologies.

Quantum process tomography, the task of learning an unknown quantum channel from black-box access, is a central problem in quantum information. In this setting, protocols with quantum memory can coherently store and jointly process quantum information obtained from multiple channel uses, whereas protocols without quantum memory must measure after each use and retain only a classical transcript of the measurement outcomes. A fundamental open question is whether quantum memory provides a query-complexity advantage even when protocols without quantum memory may adapt their experiments based on all previous outcomes with unbounded classical computational power. In this work, we show that it does. We determine the optimal query complexity of quantum process tomography without quantum memory up to a constant factor to be $Θ(d_{\mathrm{in}}^3 d_{\mathrm{out}}^3/\varepsilon^2)$, where $d_{\mathrm{in}}$ and $d_{\mathrm{out}}$ are the channel input and output dimensions, respectively, and $\varepsilon$ is the target diamond-norm accuracy. More precisely, we prove that any incoherent protocol for this task, including adaptive protocols, requires $Ω(d_{\mathrm{in}}^3 d_{\mathrm{out}}^3/\varepsilon^2)$ queries, even when each channel use may be assisted by arbitrary fresh ancilla, and we present a non-adaptive, ancilla-free incoherent protocol achieving the matching upper bound $O(d_{\mathrm{in}}^3 d_{\mathrm{out}}^3/\varepsilon^2)$. Our results thereby generalize the optimal sample-complexity bounds for single-copy state tomography, recovered as the special case $d_{\mathrm{in}}=1$. By contrast, coherent protocols with quantum memory achieve query complexity $Θ(d_{\mathrm{in}}^2 d_{\mathrm{out}}^2/\varepsilon^2)$. Hence, our results establish a rigorous learning separation between quantum process tomography with and without quantum memory.

Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features. Text and image representations extracted by frozen RoBERTa and ViT encoders are processed through bidirectional cross-attention, attentive pooling, and adaptive gated fusion. The fused feature is then amplitude-encoded into parallel quantum circuits, whose measurement readouts are concatenated with the classical representation for prediction. We evaluate PQFA on MM-IMDb and N24News through controlled comparisons using the same encoders, fusion backbone, data splits, projection dimension, and augmentation output width. PQFA consistently outperforms both the fusion backbone without quantum augmentation and a width-matched MLP augmentation baseline, while using approximately 2.2K augmentation parameters compared with 24.0K for the MLP branch. Missing-modality experiments further show improved robustness when textual or visual inputs are incomplete, with particularly clear gains when the more informative textual modality is severely degraded. Controlled ablations and feature-space analyses indicate that the improvement cannot be reproduced by random feature mappings, increased classical width, or untrained quantum transformations. Quantum-state diagnostics additionally show stable predictive performance across the tested simulated noise levels and distinct branch-specific transformations of the encoded states. These results establish PQFA as an effective and parameter-efficient strategy for post-fusion augmentation in hybrid quantum-classical multimodal learning.

Quantum distance-bounding (QDB) protocols let a verifier check that a prover is both genuine and physically nearby. During a timed fast phase of quantum communication, the verifier measures round-trip times to obtain an upper bound on the prover's distance. For a uniform comparison, we isolate the fast phase and study one-round distance-fraud (DF) and mafia-fraud (MF) games. For discrete-variable QDB, we show that these games reduce to convex optimization problems and can therefore be solved exactly with semidefinite programming; each MF value comes with an explicit attack achieving it and a matching certificate that no attack does better. This contrasts with quantum position verification, where an attack is split between two separated parties, so its optimization is nonconvex and analyses rely on relaxations. In our MF game, the cooperating pair collapses to a single sequential strategy, which keeps the game convex and its exact value computable. Across the discrete-variable protocols we examine, the best one-round DF attack succeeds with the same probability ($1/2$) for every protocol, whereas MF clearly separates the protocols. For continuous-variable QDB, we report estimated attack success probabilities from a calibrated Gaussian attack model. The benchmark covers protocols whose fast phase itself authenticates the prover; designs that follow Brands and Chaum and instead bind the fast phase with a final authenticated message, like the earliest QDB proposal, fall outside it and are treated separately. Of the four protocols studied, two had no previously known one-round attack values, and we report the first ones; for the other two, we find MF attacks with higher success probability than previously reported. Overall, one-round MF resistance depends on whether an attacker can use information revealed early by the prover to answer a fresh challenge from the verifier.

Levitating electron qubit on the surface of solid neon has recently emerged as a promising and intrinsically noise-resilient platform for quantum information processing. Their ultra-clean, inert environment suppresses conventional decoherence pathways associated with lattice disorder, charge traps, and nuclear-spin baths that limit coherence in semiconductor qubits. Yet, uncontrolled surface features such as bumps, valleys, and electrode-defined gaps can bind electrons unintentionally, contributing charge noise and inducing spin-orbit coupling mediated decoherence. To address this challenge, we propose an engineered interface in which a dielectric layer is deposited beneath the solid neon to provide an atomically smooth template, eliminating surface-roughness induced trapping. By selectively etching this dielectric layer at desired qubit locations, deterministic potential minima can be engineered to reliably capture electrons while suppressing unwanted surface bound states. We perform large-scale Schrodinger and Poisson simulation to compare the existing and proposed strategies of electron trapping on neon, obtaining good agreement with recent experimental measurements.

Quantum sensing estimates a physical parameter encoded in the state of a probe; with independent spin probes the precision follows the standard quantum limit. Studies of sensing precision often assume that the parameters entering the model, such as the noise, are known. In practice these parameters are not always known, and a mismatch between the assumed and actual values induces a systematic error. Here we study single-qubit Ramsey magnetometry of a DC magnetic field under an unknown detuning between the actual and nominal spin frequencies: A first pulse puts the qubit into a superposition of its two states, the field to be sensed then adds a relative phase during an exposure stage, and a second pulse enables the readout. In our setting, the field acts only during the exposure stage, whereas the detuning acts throughout the whole protocol. We analyze how the detuning biases the estimate, preventing the total estimation error from following the standard quantum limit. We then construct a composite-pulse preparation and readout that exploits the difference in the intervals over which the field and the detuning act to cancel the detuning to first order. We evaluate the performance of this composite-pulse protocol and show that it suppresses the detuning-induced bias.

Quantum algorithms solve certain computational problems faster than the best known classical algorithms. Many algorithms, including Shor's for factoring, Grover's for unstructured search, and the HHL for solving linear systems, rely on quantum phase estimation (QPE) as a fundamental subroutine. The QPE protocol proceeds through the initialization of a control register in a uniform superposition, controlled unitary evolution encoding the eigenphase, and a final inverse quantum Fourier transform followed by measurement to extract the phase. Here, we address a special class of unitary operators that frequently appear in quantum Fourier transform-based protocols, cyclic group representations, and periodically evolving quantum systems. We introduce a QPE algorithm that successfully reduces the circuit complexity from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ for a special class of unitary operators and implement it on a four-qubit photonic system. The four-qubit system is realised using a photon pair, with two qubits encoded in its polarization degree of freedom and the remaining two in its path modes. In contrast to previous photonic implementations of QPE based on dual-rail encoding and KLM protocol, where controlled operations are inherently probabilistic and thus reduce the overall success probability of phase estimation, our scheme is fully deterministic. Moreover, it is scalable to higher-dimensional unitaries, provided the underlying structure of the unitaries is preserved.

Rotational irrigation scheduling in water-scarce Central Asia is a densely coupled combinatorial problem: soil-moisture memory links each irrigation decision to all later days within a zone, field adjacency couples zones on overlapping window days, and rigid canal rotations quantize water delivery in time. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) by linearizing the root-zone water balance, so the quadratic crop-stress objective generates the physical couplings as 2-local Ising interactions with no higher-order terms; only the water-budget constraint requires an artificial all-to-all penalty, which we certify with an instance-adaptive weight bound an order of magnitude tighter than generic prescriptions. Every instance is built from observed data for a cotton district in Khorezm, Uzbekistan: NASA POWER meteorology, FAO-56 Penman--Monteith evapotranspiration, SoilGrids~2.0 hydraulics, measured capillary fluxes, and documented canal-rotation windows that enter as qubit-count reductions. We benchmark four tiers -- exact solvers, matched-budget heuristics, ideal-statevector quantum approximate optimization algorithm (QAOA), and noise-model plus IBM Heron execution -- and add a scaling study on soil instances up to 584 variables. Exact branch-and-bound proves optimality in seconds through 150 variables, and heuristic degradation at fixed evaluation budget is repaired by scaling the budget, so no classical scalability wall appears at deployment-relevant sizes, and none is claimed. On hardware, the informative signal is optimum-sampling enrichment over uniform sampling. We claim no quantum advantage; we deliver a physically grounded, data-complete, reproducible encoding of a societally critical scheduling problem for the quantum-utility era.

A theoretical framework for quantum annealing based on $\mathfrak{su}(3)$ algebra is proposed, and applied to the problem of overcoming first-order transitions in rugged energy landscapes. Conservation of the Casimir invariant means that one can work with irreducible representations of $\mathfrak{su}(3)$, avoiding the exponentially large Hilbert spaces of spin glass systems. In this framework, quantum drivers exhibit nonlocal properties in the sense that during annealing the wave function can be transported far away from a local minimum, thereby avoiding being trapped by it. We consider Hamiltonians with two quantum drivers and studied them numerically. It is shown that energy gap closures can be circumvented via a suitable path in the parameter space of the two drivers. Comparison with more traditional annealing driven by transverse field and antiferromagnetic operators suggests that $\mathfrak{su}(3)$ drivers are more effective in attaining the global minimum of rugged energy landscapes.

Hybrid quantum-classical application performance is increasingly limited by classical control, host-to-QPU communication, and scheduling rather than quantum execution. Existing simulators and runtime interfaces analyze individual kernels but fail to address system-topology questions, such as controller bottlenecks, diminishing returns of QPU capacity, or resource contention under heterogeneous workloads. We introduce HybridQC, a topology-aware discrete-event simulator for tightly coupled hybrid compute units (HCUs). HybridQC models HCUs as configurable graphs of classical processors, memory, controllers, quantum annealing (QA) and digital quantum computing (DQC) devices, and communication links. It decomposes jobs into typed, directed acyclic graphs of stages, ranging from input preparation to classical postprocessing, executed under interchangeable scheduling policies. Calibrated with live measurements from D-Wave (Advantage 1 and 2) and IBM (Kingston, Marrakesh, and Fez) processors, HybridQC distinguishes physical QPU occupancy from cloud wall-clock latency. The models achieve mean absolute percentage errors of 3.92%-8.04% for D-Wave QPU access time and 5.26%-19.01% for IBM quantum-seconds measurements. Workload experiments reveal that a balanced 10x HCU scaling improves makespan by only 2.19x-3.42x, while altering scheduling policies shifts makespan by up to 1.80x for a 20-job workload. Scalability varies heavily by workload dimension: a 100x input data increase yields a 306 s median runtime, whereas a 100x joint increase in circuit count, shot count, and circuit depth drives runtime to 4.806x10^7 s on an unchanged HCU. HybridQC offers a systematic framework for evaluating the topology, scheduling, and scaling limits of hybrid architectures prior to physical deployment.

Fault-tolerant quantum computing requires a continuous hybrid quantum error correction (QEC) pipeline comprising measurement readout, syndrome transport, decoding, feedback, and control. Existing QEC simulators primarily evaluate circuits, noise models, decoders, and protocol-level outcomes. System architects, however, must also understand how these workloads contend for and queue across controller, compute, accelerator, and communication resources during protected logical execution. We introduce StreamingQEC, a system-level simulator that translates fault-tolerant logical workloads into resource-constrained streaming-QEC pipelines. An explicit discrete-event simulation provides the reference execution semantics. An automatic staged-fluid mode enables faster approximate design-space exploration, while a certified recurrence mechanism compresses repeated transitions only when their scheduling state and metric contributions match those of the explicit execution trace. We assemble a decoder-runtime dataset containing 9,998 measurements, of which 8,174 are used to fit performance profiles. Recurrence reproduces the reported explicit-simulation metrics across 35 calibrated-profile configurations, as well as additional workload and cadence validation cases. For a 16-job anchor workload, it preserves 59,743,936 decoding events while achieving a 24.0x host-side speedup, and recurrent simulations scale beyond 1.22 billion events. Across 17 reference configurations, the automatics taged-fluid mode yields a mean makespan error of 2.60% and a worst-case error of 6.45%. Design-space studies reveal transfer-limited resource matching,decoder-driven pipeline stalls, and saturation of dedicated resources under microsecond-scale QEC cycles.

Large ion crystals in diverse geometries are a key resource for quantum simulation experiments. In this work, we introduce a macroscopic rf trap that supports a wide variety of one-dimensional ion configurations as well as lateral two-dimensional crystals with more than 100 ions. Our design is based on precision-machined fused silica wafers that are stacked to form the trap structure. Ten independently biased electrodes provide flexible control over the axial potential, enabling long one-dimensional crystals, isospaced ion strings, split-well chains, and two-dimensional arrays with tunable aspect ratios. We present the design and fabrication process for this trap and demonstrate the ability to tune the radial secular frequencies, detect and compensate micromotion, rotate the principal axes, and characterize trapped ion heating rates. All trap design and documentation files are freely available alongside this work, to facilitate adoption and further development within the ion trap community.

Quantum Processing Units promise speed-ups for selected computational problems, including combinatorial optimisation, but their industrial utility remains an open challenge. We study an industrial variant of the Job-Shop Scheduling Problem using quantum, quantum-inspired, and classical methods across three platforms: IBM Quantum, the D-Wave Quantum Annealer, and the Fujitsu Digital Annealer. By tailoring formulations to hardware-specific constraints, we show that hardware-software co-design is essential for solution quality and scalability. We benchmark all approaches against an exact classical solver and a MILP formulation, evaluating runtime, solution quality, and scalability. Our results indicate that quantum and quantum-inspired optimisation can support industrial solver selection, integration in classical workflows, modelling decisions, and early proof-of-concept development, while suggesting a potential path towards improved approximations for industrial scheduling.

In quantum information systems made of optical tweezer arrays of ultracold molecules, thermal motion of molecules degrades the coherence of their interactions, which limits entanglement fidelity and the concomitant scientific applicability of these systems. We show that by controlling the geometry of the dipolar interaction, even when a molecule occupies many motional states in the tweezer, coherence can be preserved. We characterize several geometries that suppress sensitivity to thermal fluctuations. We further use programmable, coherence-preserving motion of the molecules during entanglement to refocus dephasing from relative positional jitter of the tweezers, which is relevant even on the 10 nm scale. These methods yield substantially improved dipolar coherence and enable generation of two-molecule entanglement with a Bell state fidelity of $\mathcal{F}= 0.976^{+0.008}_{-0.011}$ in directly laser-cooled molecules.

I derive the charged-particle spectral edge from the quantum instrument of soft QED. I use two projections of that instrument. Tracing over unresolved photons gives the reduced hard-sector channel. Pushing the outcomes to total energy gives the inclusive energy distribution. Its Laplace exponent is fixed by the diagonal soft intensity. For ${\rm d} N_h(ω)=η_h{\rm d}ω/ω+{\rm d} N_{h,\mathrm{reg}}(ω)$, I obtain $ρ_{\mathrm{inc}}(s)\sim Cθ(s-m^2)(s-m^2)^{-1+η_h}$. I retain the coherence kernel and derive hard-sector dephasing and the spectral edge from two contractions of one soft environment. The diagonal coefficient $κ_{aa}$ fixes the endpoint exponent, while $\frac12(κ_{aa}+κ_{bb}-2\operatorname{Re}κ_{ba})$ fixes the dephasing exponent between hard alternatives. I then classify infrared energy marginals, derive the finite-resolution residue $Z(μ)=(μ/Λ)^{η_h}$, prove stability under infrared-integrable perturbations, and separate the bath exponent from a hard threshold exponent. For the one-electron spectral measure, the hard threshold factor is regular. The resulting edge has the local power law of a gapped unparticle spectrum, while its exponent remains a response coefficient of the unresolved photon sector.

Efficient communication between distant qubits is one of the central challenges in scaling quantum processors. Although engineered spin chain protocols have been extensively investigated theoretically, their experimental realization has remained comparatively limited. Here, we experimentally realize engineered quantum communication protocols through digitally simulated spin Hamiltonian on IonQ's Forte 1/ Forte Enterprise 1 trapped-ion quantum processor. Combining exact numerical simulations with quantum hardware experiments, we benchmark uniform nearest-neighbour and engineered coupling profiles and demonstrate that engineered interactions significantly enhance the fidelity of quantum state transfer. We further show that exploiting the commutation structure of the spin Hamiltonian enables a parallel Trotter decomposition that more faithfully reproduces the target dynamics while substantially reducing the circuit depth and execution time compared to the conventional sequential implementations. Our results demonstrate that programmable quantum processors can effectively realize and efficiently implement quantum communication protocols, bringing Hamiltonian-based quantum communication closer to practical quantum technologies.

Parameterized quantum circuits~(PQCs) constitute a central building block of variational quantum algorithms~(VQAs) and quantum machine learning~(QML) methods. Existing ansatz designs often adopt hardware-agnostic or simplified 1D chain/ring entanglement patterns. However, as quantum hardware continues to develop, native 2D connectivity patterns, such as planar superconducting-qubit architectures, are becoming increasingly important. Inspired by this hardware structure, we construct a native 2D pairwise ansatz and compare its expressibility and trainability with representative 1D ansatze at identical layer depths, despite their different circuit depths. For the fixed 16-qubit system, the 2D ansatz has the smallest KL divergence at $L=1$ and $2$, and its second-order frame potential approaches the theoretical lower bound more rapidly at shallow layer counts than the frame potentials of the three 1D ansatze. We also evaluate the gradient variance of the Pauli-$Z$-string expectation value $\langle Z_0\otimes\cdots\otimes Z_{15}\rangle$ with respect to the first $R_y$ angle. For this Pauli-$Z$ string and fixed parameter, the gradient variance is smaller for the 2D circuit at $L=1$--$4$. The differences narrow at $L=5$, and the four ansatze yield statistically compatible variances at $L=6$.

A Lorentz boost acts on the canonical spin of a massive particle through a momentum-dependent Wigner rotation. We show that, for one fixed observer boost, reducing over an uncertain momentum can strictly contract every pairwise spin-state trace distance without producing a channel that is degradable from the less contracted one. For spin $1/2$, we first characterize the exact inversion-symmetric channel cone generated by a fixed Wigner angle and transverse momentum directions. Inside this cone lies the Pauli family $M_α=\operatorname{diag}(1-α,1-α,1-2α)$, $0\leqα<1/2$. For $0<α<β<1/2$, all trace distances between distinct spin states are strictly smaller after $M_β$ than after $M_α$, yet the unique linear post-processing factor has a negative normalized Choi eigenvalue. We solve the optimization over all physical converters exactly: $\frac{1}{2}\inf_{Λ\in\mathrm{CPTP}}\|Φ_β-Λ\circΦ_α\|_\diamond=\frac{α(β-α)}{2-3α}$, whereas the reverse deficiency is $β-α$. Thus the identity dominates the family, while all positive-noise members are pairwise incomparable under CPTP post-processing. The ideal construction is realized as the narrow-packet limit of pure, normalizable five-component momentum states, and explicit perturbation and finite-shot tomography bounds certify an open set of examples. Separately, every nonidentity member fails embedding in a time-homogeneous Pauli-diagonal Lindblad semigroup. Hence ordering all unassisted spin distinguishabilities does not determine the quantum statistical post-processing order.

Credit Valuation Adjustment (CVA) requires repeated risk-neutral expectation estimation, making it a natural test bed for quantum amplitude estimation, whose coherent amplification can in principle reduce Monte Carlo sampling cost. Whether this advantage survives realistic financial encoding and noisy hardware remains open. We develop an end-to-end, noise-aware quantum workflow for CVA, covering market calibration, discretisation, oracle construction, hardware execution and error-budget analysis. The model combines a correlated two-asset exposure with discount and default factors, encoded through a QCBM-based joint time-market distribution and controlled payoff rotations. We introduce contrast-aware Bayesian iterative quantum amplitude estimation (CABIQAE), which incorporates experimentally calibrated Grover-contrast loss into Bayesian inference and circuit-depth selection. Hardware-calibrated experiments show that CABIQAE exploits the limited amplification available on current devices more effectively than noise-agnostic alternatives and achieves a much lower classical post-processing runtime than the noise-aware BAE baseline. The analysis further decomposes the total CVA error into statistical, encoding, discretisation and hardware contributions. The full CVA oracle remains limited by circuit depth and discretisation resolution.

Neutral-atom quantum processors are rapidly scaling toward system sizes of more than ten thousand qubits, allowing for the realization of a new class of quantum computing algorithms and quantum simulation experiments. However, current neutral-atom platforms generally have to find a compromise between the optical accessibility and the storage time of atoms in optical potentials, limiting the available qubit numbers. Here we report on the operation of a novel, cryogenically enhanced, neutral-atom apparatus that overcomes these apparently conflicting requirements. We demonstrate vacuum-limited trapping lifetimes of up to two hours of single $^{88}\mathrm{Sr}$ atoms in an optical tweezer array while preserving full optical access and without the need for complex cryogenic enclosures. Our measurements show that exceptionally long single-atom lifetimes can be achieved with a relatively simple cryostat design. Our architecture can be straightforwardly ported to other atomic species and shows a viable path for scaling up to sorted arrays of tens of thousands of atoms.

Unlike in finite dimensions, quantum information in continuous-variable systems has the peculiar feature that without imposing physical constraints, the sample complexity of state tomography can be unbounded. Remarkably, this is even the case for state-of-the-art protocols for learning Gaussian states, which have finite-dimensional descriptions: the best known rates scale with $\log \log E$, where $E$ is the energy of the system. We prove this is not an artifact of existing analyses, but a fundamental limitation of the measurements used. We show: (1) Any protocol that uses Gaussian measurements, even entangled or adaptively chosen ones, must incur a $\log \log E$ dependence. This answers an open question posed by a number of previous works. (2) There is a smooth tradeoff between the number of rounds of adaptivity and the energy dependence, and we give a matching protocol achieving this interpolated rate. (3) With highly entangled, non-Gaussian measurements, one can learn $n$-mode pure Gaussian states with $O(n^2 / ε^2)$ samples, independent of $E$. This answers an open question posed by Chen et al. (4) A simple protocol based on the single-copy canonical phase POVM of Holevo and Helstrom learns single-mode pure Gaussian states with $O(1/ε^2)$ samples, again independent of $E$. Our results clarify the role of energy in bosonic state tomography and shed new light on the intriguing interplay between adaptivity, entanglement, and magic in quantum learning.

A central model in quantum machine learning is the quantum neural network (QNN), whose design requires balancing expressivity and trainability. Technically, expressivity is studied through circuit-function analysis, such as quantum signal processing, while trainability is analyzed using dynamical-Lie-algebra (DLA) methods. To support certified QNN design, we formalize these major components of QNN theory in a connected lean 4 development checked by a proof kernel, where every analytic input is either proved or exposed as a named hypothesis. On the expressivity side, we prove exact if-and-only-if characterizations of single-qubit QNNs, a resource-counted quantum phase processing theorem, and an overparameterization ceiling that bounds the quantum Fisher information rank by the DLA dimension. On the trainability side, we derive the direct-sum loss-variance law through a de-circularized second-moment interface. A parameterized Casimir-uniqueness engine discharges the required inputs for fully controllable, orthogonal, and matchgate circuit families, while single-qubit and product-Clifford ensembles close the two-design assumptions directly. A capstone theorem pairs the conditional variance law with exact loss reconstruction in DLA coordinates. The development record identifies eight corrections and clarifications that were not explicit in the informal arguments. We expect this work to provide a machine-checkable foundation for QNN theory and a step toward AI-assisted or automated design of quantum machine learning algorithms.

A finite quantum reference token with group-valued misalignment induces a random-unitary channel, but optimal degradation is generally an optimization over all quantum post-processings. For a unitary representation U of a finite group G, we prove that the following conditions are equivalent: U contains every irreducible type; one ancilla-assisted input has an orthonormal G-orbit; signed group measures embed isometrically into channels in diamond norm; and, for every pair of noise laws p,q, $\inf_{Λ\in\mathrm{CPTP}} \frac{1}{2}\|Φ_q^U-Λ\circΦ_p^U\|_\diamond =\min_{r\in\mathcal{P}(G)}\frac{1}{2}\|q-r*p\|_1$. Thus representation completeness is the exact carrier condition for universal reduction of quantum post-processing to classical convolution. We determine the minimum ancilla dimensions for an orthogonal orbit and for an invariant calibration seed. For an incomplete carrier, with visible Plancherel dimension S(U), we derive the exact conditional-expectation distance $\frac{1}{2}\|\operatorname{id}-Φ_u^U\|_\diamond=1-1/S(U)$ and an explicit quantum--classical deficiency gap. For irreducible carriers the deficiency is obtained in closed form; the faithful two-dimensional representation of $S_3$ yields an exact ten-percent reduction relative to classical convolution. We also characterize law identifiability through the conjugation representation, provide finite linear programs and decision witnesses, and establish both a finite-dimensional obstruction and stable visible-band reconstruction for infinite compact groups. Deterministic ancillary code reproduces the finite-group examples and numerical regression checks.

We demonstrate a nonreciprocal quantum Mpemba effect. Consider a broad class of open quantum systems, each coupled to two isomorphic reservoirs through symmetric ports. Interchanging the parameters of the two reservoirs -- a discrete operation we call the swap -- turns the quantum Mpemba effect on or off without changing the initial states. The swap modifies the Liouvillian, yet a structural symmetry pins the eigenvalues while rotating only the eigenvectors. The nonreciprocity therefore leaves no trace in the spectrum and is carried entirely by the eigenvectors. Concretely, the swap alters the far state's projection onto the slowest mode, switching whether it bypasses the slowest relaxation channel. At a Liouvillian exceptional point, the far state's relaxation switches from bypassing the slowest mode to avoiding the critical slowing, with the on--off contrast intact. There the spectrum-independent mechanism takes its purest form.

Nematic symmetry entails conserved quantized quantities such as number of topological defects and vorticity cells. Correspondingly, countless quantum analogies have been found in Active Nematics. We formalize Active Nematics and Liquid Crystal theory into the framework of Quantum Mechanics by introducing a complex valued Nematic Wavefunction to the Beris Edward equations, thus splitting spatiotemporally varying nematic systems into quantized states. We obtain the Planck's energy-frequency relationship for active micro-swimmers such as peristaltic worms and bacterium as a consequence of local complex phase-symmetry of the governing equations, similar to the gauge formulation of Electromagnetism. For organisms operating on diffusive chemotaxis, we obtain predator-prey dynamics that evolve to maximize/minimize pheromones field gradient overlap. Furthermore, when quantizing beating hearts, similar to the orbitals of hydrogen atoms, the state-function allows us to characterize hearts not only through the rhythm, but also the spaciotemporal distribution of contractile activity of various harmonics among healthy and unhealthy hearts.

Logical entangling gates are a major source of physical spacetime overhead in fault-tolerant quantum computation. Phantom codes reduce this cost by implementing every ordered in-block logical CNOT through physical qubit permutations and Pauli-frame updates. Whether this mechanism can coexist with the low-weight stabilizer structure of qLDPC codes is a central question for low-overhead fault-tolerant architectures. We give a deterministic answer within binary CSS hypergraph product (HGP) codes. Up to natural equivalences, the simplex-repetition family is the unique HGP family satisfying the phantom condition. We then evaluate this family under circuit-level noise in logical GHZ-state preparation and Trotterized many-body quantum simulation. The codes retain low-weight stabilizer checks and yield concrete advantages over rotated surface-code baselines in both benchmarks. Reconfigurable neutral-atom arrays offer a natural setting for this approach, supporting nonlocal qLDPC operations while enabling in-block logical CNOTs without additional physical operations. Together, these results make precise how permutation-based logical entangling constrains code design within the HGP framework, demonstrate the circuit-level benefits of the unique family, and guide the search for phantom qLDPC families with better asymptotic parameters for low-overhead fault tolerance on neutral-atom hardware.

Comparing two noisy quantum reference frames as statistical experiments depends on the dimension of the ancillary memory available to the decision procedure. For finite-dimensional channels A and B with invertible A, we show that exact simulation of all measurements assisted by an r-dimensional ancilla is equivalent to r-positivity of the unique factor Gamma = BA^{-1}. The hierarchy can be realized by physical channel pairs: every unital, trace-preserving map that is k-positive but not (k+1)-positive embeds as the factor between the channels D_a and Gamma composed with D_a on an exact interval determined by the smallest Choi eigenvalue. For depolarizing source and target channels D_a and D_b, including negative and singular source parameters, the phase boundary is $\mathcal{D}_a \succeq_r \mathcal{D}_b \Longleftrightarrow -1/(dr-1) \leq b/a \leq 1$ for $a\neq 0$. We derive closed formulas for the restricted level-r deficiency and for the distance to every physical post-processing, $δ_{\mathrm{phys}}(\mathcal{D}_b\mid\mathcal{D}_a)=(1-1/d^2)\operatorname{dist}(b,I_a)$, where $I_a=\operatorname{conv}\{a,-a/(d^2-1)\}$. The largest physical conversion cost hidden from all tests through level k is $(d-k)/[d(d^2-1)]$. An untouched m-level spectator changes the first detecting external level from k+1 to $\lfloor k/m\rfloor+1$. A transpose--depolarizing construction shows that the separation is not confined to depolarizing factors. The results quantify the distinction between ancilla-restricted statistical simulation and implementation by a single quantum channel.

How large can a set of mutually orthogonal quantum Latin squares (MOQLS) get? We show that a set of n - 2 MOQLS of order n is necessarily classical and construct large non-classical sets of MOQLS of orders that are prime powers, improving both the previously known lower and upper bounds.

Recently, it has been shown that protocols utilizing infinitely fast controls, such as quantum error correction, can in principle restore Heisenberg-limited frequency estimation in the presence of a broad class of non-Markovian noise models arising from coupling to finite-dimensional environments. However, these controls differ substantially from those used to address Markovian noise, and their underlying physical mechanism remains unclear. In this work, we establish a direct connection between these protocols and the quantum Zeno effect, and extend the framework to infinite-dimensional environments. We delineate three types of constructions: (a) protocols that rely solely on measurements, (b) protocols using an active recovery after measurements and (c) protocols using dynamical decoupling and rigorously analyze the performance of each when controls can be applied at only a finite rate. While protocols relying purely on measurements can be engineered for noise models where active recoveries are fundamentally impossible, they result in the quantum Fisher information exhibiting a quadratically worse dependence on the control frequency. Surprisingly, while dynamical decoupling protocols are possible whenever protocols relying only on measurements can be engineered, the quantum Fisher information has the same dependence on control frequency as the active recovery protocols. Numerical simulations suggest that the improvement offered by dynamical decoupling may work in regimes beyond the perturbative setting where our rigorous theorems apply.

Let $\left(M, \mathcal{O}_M \right)$ be a smooth algebraic variety over field $κ$ of characteristic $0$ with an algebraic symplectic form $ω$, or a complex manifold with a holomorphic form $ω$. Furthermore, let $E$ be a vector bundle over $\left(M, \mathcal{O}_M \right)$ and $\mathcal{O}_{\hbar}$ a deformation quantization of $\mathcal{O}_M$ compatible with $ω$. Assuming that $E$ possesses a deformation quantization to order $\hbar^k$ we consider the problem of extending it to order $\hbar^\ell$ for $\ell > k$, and establish triviality of an obstruction class as a necessary condition for this extension to exist. Furthermore, in the case $\ell \le 2k+1$, we prove that this condition is also sufficient.

We present an approach to unitary synthesis that implements an arbitrary $n$-qubit unitary operator $U$ by a Clifford+T circuit with T-count $\widetilde{O}(2^n d_F^{\mathcal{C}}(U))$, where $d_F^{\mathcal{C}}(U)$ is the Frobenius norm distance of $U$ to the Clifford group. The T-count is shown to be near-optimal when $d_F^{\mathcal{C}}(U)$ is a constant. Our approach improves the previous best upper bound $\widetilde{O}(2^{4n/3})$ due to Tan (2025) for a large class of unitary operators $U$ as long as $d_F^{\mathcal{C}}(U) \ll 2^{n/3}$.

In this paper, we show that for Schrödinger operators with weakly correlated ground states, the Hohenberg-Kohn theorem holds within the maximal class of form-bounded external potentials if and only if the single-particle density is positive quasi-everywhere. Furthermore, we show that these conditions are satisfied for the ground state of non-interacting Schrödinger operators with a discrete ground state energy. Consequently, we establish the Hohenberg-Kohn theorem for non-interacting systems, and therefore the uniqueness of the Kohn-Sham potential, within the maximal class of Laplace form-bounded potentials. The key ingredient to establish these results is a characterization of weakly correlated regular states, whose proof relies on classical potential theory. Moreover, our proof reveals that, in the continuum setting, the fundamental mechanism underlying the Hohenberg-Kohn theorem is the (quasi)-unique continuation of the density rather than of the many-body wavefunction.

We show that geodesic motion in the Riemannian manifold of quantum states provides a direct route to time-independent counterdiabatic driving. Using the relation between the counterdiabatic Hamiltonian and the quantum metric tensor, we prove that a constant-speed geodesic makes the Hilbert-Schmidt norm of the counterdiabatic Hamiltonian constant. For effective two-level systems whose counterdiabatic correction has a fixed operator direction, this further implies that the full counterdiabatic Hamiltonian itself is time independent. We illustrate this result with the Landau-Zener model, three-level Stimulated Raman adiabatic passage and a collectively driven Rydberg ensemble in the blockade regime. Limitations of this approach in realistic many-body systems are discussed, where the two-level reduction is only emergent and leakage out of the effective subspace bounds the achievable speedup. In all cases, time-independent counterdiabatic driving achieves unit-fidelity state preparation on timescales substantially shorter than conventional adiabatic protocols while replacing temporally shaped auxiliary controls by fixed-amplitude fields.

We investigate dynamical quantum phase transitions (DQPTs) in finite-dimensional systems prepared in thermal equilibrium states and subjected to a sudden quench. A mixed-state Loschmidt amplitude is constructed from first principles within a metric-stationary pseudo-Hermitian framework, providing a self-contained derivation of the finite-temperature quench dynamics. Applying this framework to an $N$-level model consisting of a two-level sector coupled to $N-2$ spectator states, we find that temperature controls the DQPTs through the redistribution of thermal weights among the eigenstates. This mechanism leads to a dimensionality-dependent threshold temperature that becomes finite when the Hilbert-space dimension reaches five, above which the Loschmidt amplitude loses all real zeros and the DQPTs are fully suppressed. The thermal suppression mechanism suggests a general principle for controlling dynamical criticality through thermal occupation, while the quasi-Hermitian framework provides the self-consistent foundation for its rigorous derivation.

Quantum key distribution (QKD) promises information-theoretically secure communication, but future networks must bridge fiber and free-space links that naturally employ different photonic encodings, namely time-bin in fiber and polarization in free space. Here we demonstrate a complete hybrid fiber and free-space QKD link that bridges both media within a single end-to-end protocol, converting between the two encodings entirely in the optical domain. Using the decoy-state BB84 protocol operating at 1550 nm, we demonstrate continuous secure-key generation over a 90 m outdoor free-space link. The system operates across atmospheric conditions spanning more than two orders of magnitude in the refractive-index structure parameter Cn^2, from strong daytime turbulence to quiescent nighttime conditions, and we further validate photon-level operation over a 750 m free-space extension. Throughout, the link maintains a session-mean quantum bit error rate (QBER) of 5.6-6.8%, well below the 11% BB84 security threshold. The encoding conversion is performed entirely in the optical domain without measurement or state reconstruction, preserving the security assumptions of the BB84 protocol. Consequently, the time-bin-to-polarization (T2P) and polarization-to-time-bin (P2T) converters remain part of the untrusted quantum channel rather than trusted intermediate nodes. These results establish secure photonic encoding conversion as a practical interface between fiber and free-space quantum communication platforms, providing a building block for future quantum networks applications.

This article explores the potential of Quantum Machine Learning (QML), specifically assessing a Quantum Support Vector Machine (QSVM) and a Variational Quantum Classifier (VQC) for detecting anomalies in real-world financial transaction data. While these QML methods outperform statistical methods, they fall short of cutting-edge deep learning techniques. To bridge this gap, we propose a hybrid quantum-classical ensemble framework that leverages the strengths of both domains. We demonstrate its effectiveness in detecting phishing in Ethereum transaction networks by combining complementary algorithms. The QSVM, whether used individually or in an ensemble, consistently delivered the lowest false negatives and higher recall rates, that are crucial for anomaly detection. To enhance individual models, we encoded the data using novel cascaded Quantum Random Access Coding (QRAC) schemes and compared it with the popular encoding ZZ feature map on both simulators and the IBM Heron quantum processor. For both QSVM and VQC, we consistently observed improvements (13% for QRAC-VQC and 3% for QRAC-QSVM) of QRAC over the ZZ feature map. Notably, certain QML algorithms exhibit remarkable resilience on the IBM Heron quantum processor, approaching simulator-level performance on devices with high quantum volume. This observation underscores the promise of QML despite hardware limitations.

The resolution of the identity is typically utilized as a passive representation tool in standard quantum mechanics textbooks. In this paper, we reinterpret this traditional perspective by introducing the resolution of the identity associated with Glauber coherent states as a direct generator of exact complex Gaussian integral identities. By systematically projecting the continuous coherent-state completeness relation onto the discrete Fock basis, highly generalized Gaussian integral relations emerge naturally as a direct consequence of state preservation, rather than being introduced as independent mathematical postulates. We demonstrate that this overarching formal structure simultaneously dictates both discrete (Kronecker delta) and continuous (Dirac delta) localization kernels under appropriate basis projections, providing a sharp conceptual contrast between the structural features of overcomplete and strictly orthogonal representations. Crucially, we highlight an intriguing non-commuting behavior in the parameter limits of the master identity. Requiring only the elementary properties of coherent states and the Dirac formalism, this approach offers a transparent and visually intuitive illustration of how Hilbert-space completeness inherently encodes vast libraries of exact, solvable mathematical relations -- providing a unifying perspective suitable for advanced undergraduate and graduate instruction.

This study investigates the use of quantum annealing for particle track reconstruction in strip-type gaseous detectors. In such detectors, ghost hits and multiple hit combinations can turn pattern recognition into a combinatorial optimization problem. We formulate two reconstruction subproblems as quadratic unconstrained binary optimization problems. The first subproblem selects detector hits associated with a single photon track inside a localized candidate region. The second subproblem selects cluster triplets from different detector layers so that multiple track candidates can be handled within a single quantum processing unit(QPU) submission. The proposed formulations are tested using simulated DAMSA detector events. For the single track hit selection task, the QPU based reconstruction gives position and angular resolutions close to those obtained with a Kalman based reconstruction. In the simultaneous association task, valid cluster triplets are first extracted from the QPU samples and then connected using an association rule based on graph connectivity to construct track candidates. The DAMSA event topology studied here has low pileup and is dominated by the two photon signal from axion-like particle(ALP) decay. In this setting, the results show that the QUBO formulations can reproduce local reconstruction decisions. This provides a practical basis for further studies of reconstruction methods that combine quantum and classical computing in more complex tracking environments.

We present a variational quantum framework for finite-horizon quantum control based on hardware-efficient ansätze. The objective is to steer a quantum system from a given initial state to a desired target state over a fixed time horizon by minimizing a terminal cost defined in terms of state fidelity. Instead of explicitly synthesizing time-dependent control fields or enforcing Hamiltonian reachability constraints, the proposed method reformulates the control objective as a variational optimization problem in which a hardware-efficient parameterized quantum circuit provides a surrogate parameterization of the terminal evolution. The circuit consists of alternating layers of single-qubit rotations and entangling gates, whose parameters are optimized using classical routines to minimize the terminal infidelity. This formulation avoids reliance on problem-specific or physics-inspired ansätze, providing a flexible and implementation-friendly approach compatible with near-term quantum devices. Numerical experiments on multi-qubit state-transfer benchmarks demonstrate high-fidelity state transfer while highlighting the trade-off between ansatz expressivity, optimization complexity, and scalability with respect to system size and circuit depth.

Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.8M-parameter encoder-decoder transformer with structured circuit tokenization, evaluating on parameterized circuits (2-6 qubits) and Clifford+T circuits (3-6 qubits). On parameterized circuits, a hybrid approach -- structure from the transformer, angles from classical optimization -- achieves median fidelity 1.000 on 3-6 qubit circuits. On Clifford+T circuits, where all gates are discrete and no post-processing is possible, the model learns valid syntax and accurate T-Count statistics, yet exact equivalence degrades sharply with target length -- from 88% on circuits with <=9 gates to near zero beyond 26 gates. We trace this failure to autoregressive drift: early-token divergence cascading irrecoverably through left-to-right decoding. Two levers partially mitigate the drift: inference-time strategies that generate multiple candidates and select via equivalence verification raise exact-match rates from 7% to 22.5%, while scaling training data by 2.5x pushes them to 39.5%. Yet the degradation with target length persists -- even with more data, exact equivalence drops from 94% on short circuits to under 4% beyond 26 gates. The contrast between settings is our central finding: when approximate outputs can be rescued by post-processing, the transformer succeeds; when exact discrete correctness is required, autoregressive drift limits reliability, with both inference-time search and data scaling as effective levers while training-side fine-tuning and model-level diversification are not.

Accurate knowledge of the microwave power delivered to a cryogenic device under test (DUT) is essential for the characterization and operation of superconducting quantum circuits. However, this information is difficult to obtain inside dilution refrigerators because of distributed attenuation, impedance mismatch, switch-path repeatability, and temperature-dependent microwave components. This paper presents an in situ measurement method for RF power at the cryogenic device plane. The method uses a custom variable temperature stage (VTS) as a cryogenic thermal-transfer element. The TVS is alternately heated by a four-wire DC heater and by microwave power dissipated in a 20 dB pass-through attenuator. By fitting the thermal transients and comparing the corresponding steady-state temperatures, the absorbed microwave power is inferred from a directly measured DC electrical power through an AC/DC substitution procedure. The finite reflection and transmission of the attenuator are then accounted for by cryogenic two-port scattering-parameter measurements based on a switch-assisted Short--Open--Load--Reciprocal calibration, so that the result is referred to the DUT reference plane. The system is demonstrated in a dilution refrigerator with powers between -43 and -58 dBm at the DUT input plane. The demonstrated relative standard uncertainty ranges from about 2% at -43.9 dBm to about 40% at -57.6 dBm. The proposed approach combines thermal RF power transfer, cryogenic S-parameter correction, and uncertainty evaluation in a measurement architecture compatible with quantum-device experiments, providing a practical route toward traceable microwave-power calibration at millikelvin stages.

Restart of a quantum process is typically modeled as a global reinitialization that erases the system's entire history. Here we introduce subspace restart, a protocol that periodically resets only the internal degrees of freedom while preserving the spatial distribution, as a tunable knob for the quantum-to-classical crossover. Using the discrete-time quantum walk as an example, we show that this selective reset drives the walker into an engineered drifted-diffusion regime. This phenomenon can be understood by a Huygens-Fresnel mechanism, where each restart fragments the wave function into a set of independent secondary sources to screen long-range correlations and isolate a robust classical backbone, whose drift and diffusivity are set by the geometric orientation of the initial coin and the restart period. Residual quantum interference, confined to effective light cones, survives only as a short-range correction that renormalizes these coefficients and imprints periodic modulations on the cumulants. Our results establish subspace restart as a route to controlling the quantum-to-classical crossover in synthetic lattices.

Thermal noise ordinarily suppresses quantum entanglement. We show that a strong non-Abelian conservation law can convert local thermal fluctuations into an unbounded operational resource. For a broad class of finite-range $SU(2)$-invariant spin chains restricted to the global-singlet sector, an explicit representation-space protocol yields $Y_N=\frac12\log_2 N+O_β(1)$, and hence $E_{\mathrm{D}}\geq Y_N$, throughout a finite high-temperature interval. Local thermal fluctuations produce subsystem spins $j\sim\sqrt N$, whose globally locked irreducible representations contain $\log_2(2j+1)\sim\frac12\log_2N$ ebits. An exactly solvable dimer chain exhibits a sharper effect: its zero-temperature state is unentangled across the cut, whereas every fixed $T>0$ produces $E_{\mathrm{D}}=\frac{1}{2}\log_2 N+C(T)+o(1)$, with crossover scale $T_*(N)\simΔ/\ln N$. Exact diagonalization of a nonintegrable chain is consistent with the predicted scaling. Thus heating can activate system-size-diverging distillable entanglement across a macroscopic bipartition when thermalization is confined by a non-Abelian conservation law.

Quantum PDE solvers are difficult to evaluate in practice because published studies use different discretizations, output models, reconstruction rules, and hardware assumptions. We present a reproducible, application-driven benchmark for the 1-D Dirichlet heat equation that compares eleven kernels under the same problem instances and readout contract. The benchmark covers coherent linear solvers (HHL, QSVT, and QLS-Fourier), VQLS, imaginary-time methods (QITE, var-QITE, and AVQDS), real-time Hamiltonian simulation and unitary dilations (Hamiltonian simulation, Schade-Hamiltonian, and Schr"odingerisation), and the spectral quantum simulation method (QSM). We use three initial conditions, four grid sizes from $n=4$ to $7$ qubits ($N=16$ to $128$), a CFL-like ratio $r\approx0.4$, and final time $T=1$. Statevector, ideal-shot ($10^5$ shots per step), and noisy Aer backends separate algorithmic, sampling, and device-noise errors. On statevector, QSM and Schade-Hamiltonian reproduce the semi-discrete reference to floating-point precision, Schr"odingerisation reaches approximately $10^{-4}$ error, and QITE is the strongest non-transform method for smooth data. Under the fixed-shot setting, HHL degrades to approximately $0.79$ relative $\ell_2$ error, while several low-depth or postselected methods become readout-limited. A norm-mismatch ablation attributes 23--29% of the $n=7$ smooth-initial-condition error of Hamiltonian simulation, AVQDS, and QLS-Fourier to reconstruction normalization. Compact observables, including total thermal energy and individual Fourier-mode weights, require 1--3 orders of magnitude fewer shots than full-field reconstruction. The resulting public benchmark provides a practical guide for selecting quantum PDE solvers.

Inelastic light scattering is a central tool for sample characterization and label-free imaging across the physical and life sciences. Recent work has suggested that scattered light can also exhibit nonclassical correlations. Here, we develop a microscopic theory of Raman scattering that connects naturally to established descriptions of entangled-photon generation in three- and four-wave mixing, while capturing essential differences arising from the resonant, dissipative character of Raman scattering. Using a cumulant-expansion approach, we analyze the entanglement structure of the scattered sidebands and identify signatures that distinguish Raman-mediated correlations from those generated in conventional off-resonant nonlinear wave mixing. In particular, we show that Raman scattering induces chiral couplings between Stokes and anti-Stokes sidebands, leading to nonreciprocal amplification of coherent seed fields. These results establish a theoretical framework for Raman-based quantum photonic protocols and suggest routes toward quantum-enhanced Raman spectroscopy and imaging.

The Swing-UP of quantum EmitteR population (SUPER) scheme has recently been proposed as a deterministic method for the preparation of collective radiative states in two strongly dipole-coupled quantum emitters (Phys. Rev. Res. \textbf{8}, 013179 (2026)). Here, we extend this approach to an equilateral subwavelength triangular trimer of dipole-coupled two-level quantum emitters (QEs), loosely inspired by biological light-harvesting ring geometries. Using tailored, time-overlapping, red-detuned ultrashort SUPER pulses, we numerically investigate the selective preparation of collective target states. We find that both the state selectivity and the preparation efficiency depend strongly on the inter-emitter spacing. In particular, at deep-subwavelength separations, the symmetric collective state can be deterministically prepared with near-unity efficiency, whereas the inversion efficiency and state selectivity are significantly lower at larger inter-emitter separations. Furthermore, this state preparation technique inherits a certain degree of robustness against reasonable static position imperfections and on-site frequency inhomogeneities of the individual QEs. Our results demonstrate that deep-subwavelength triangular trimers and, more broadly, highly compact ring geometries are excellent candidates for the deterministic preparation of collective radiative states via SUPER excitation. These predictions could be realized with solid-state emitters and molecules. Our findings offer a route toward the direct probing of the `pure' electromagnetic layer of interaction in biological and bio-inspired synthetic nanophotonic ring configurations, with possible relevance in photonics, quantum information processing, and metrology.

Metrology with undetected photons is an emerging technique that leverages quantum effects and photon correlations (entanglement) to retrieve valuable information in a target spectral range (e.g., mid-infrared, mid-IR) using measurements in an easily accessible domain (e.g., visible, near-IR). The underlying quantum process of spontaneous parametric down-conversion (SPDC) is utilized to generate non-degenerate correlated signal and idler photons to serve as detection and probing photons, respectively. Sensing with undetected photons enables important advantages, such as ultra-low probe powers, room-temperature operation, and shot-noise-limited detection. In this contribution, we apply a quantum nonlinear interferometer based on an SPDC source to perform applied mid-IR spectroscopy, mid-IR microscopy, and mid-IR optical coherence tomography (OCT) as among the most promising techniques for quantum-based routine non-destructive testing. Moreover, we characterize the system, benchmark it against classical systems, and provide a prospective outlook for this new technology.

Quantum metrology protocols typically consist of four steps: state preparation, evolution, measurement, and data processing. Often, the first three steps are prioritised when designing a scheme as they contain all the quantum elements. The data analysis is generally considered an add-on with an implicit assumption that this step is well behaved and so standard data techniques can be applied. However, the situation can be more nuanced, such as when the available data are limited. In limited-data quantum metrology the choice of data analysis technique and cost function of the estimator is of great importance, and a reliable prior distribution of the unknown parameters is required for Bayesian analysis. An interesting question is what we should do when no such prior is available. In this work, we consider how the James-Stein estimator can give significant advantages when measuring multiple unknown parameters with limited data and, importantly, does not require any prior distribution. We demonstrate the advantage by applying this methodology to simple quantum metrology schemes.

Recent advances are enabling quantum emitters to interact with photonic crystals, whose electromagnetic modes exhibit complex dispersion relations, spatial mode structure, and polarization textures. However, modeling light-matter behavior in these systems faces a persistent trade-off: electromagnetic approaches based on Maxwell-equation solvers provide realistic vectorial descriptions but are difficult to integrate with quantum many-body and non-perturbative methods, whereas simplified quantum-optical lattice models are tractable but typically rely on scalar and spatially independent light-matter couplings that miss essential features of these structured photonic environments. Here, we introduce a constructive framework to derive quantum-optical lattice descriptions that overcome this trade-off. Combining symmetry-constrained tight-binding constructions with numerically computed photonic band structures and field profiles, our method yields minimal, symmetry-enforced lattice Hamiltonians that reproduce the target photonic dispersion while retaining the mode-resolved (position- and polarization-dependent) structure of the light-matter coupling. We show that these models recover Green's-function-based emitter dynamics in the perturbative regime, while providing access to non-perturbative quantum dynamical simulations beyond emitter-only descriptions. As a proof of principle, we apply the framework to a two-dimensional photonic crystal and show that it captures polarization-dependent directional emission inaccessible to scalar models, while enabling the analysis of non-Markovian light-matter dynamics and entanglement. Our results provide a practical bridge between classical electromagnetic simulation tools and quantum-optical many-body and non-Markovian modeling in photonic crystal settings.

In this work we derive statistical inequalities whose violation is equivalent to the impossibility of describing the data by a genuine joint probability distribution. So they are witnesses of the failure of a common probability space. We examine whether the most significant quantum quasidistributions violate these inequalities. We examine also whether these inequalitites are violated by the joint distributions derived form a noisy joint measurement. As a relevant example we find violations for the maximally mixed state, as well as cases where all system states violate them. This points to the idea that these results are more than a property of the system states, but are instead a property of the statistical structure of quantum mechanics.

In this work, we develop and study the classical Lanczos algorithm allowing us to define Krylov complexity using the symplectic structure of phase space: Poisson brackets take on the role of the quantum commutators and phase-space integrals furnish the inner product needed to define the Lanczos recursion. We show, using general methods of quantum mechanics in phase space, that the $\hbar \to 0$ limit of the usual quantum mechanical Krylov framework smoothly goes over into the classical one. In theories with well-defined semiclassical limits, we show that classical Krylov complexity accurately approximates quantum complexity at early enough times, and thus is a useful characteristic of early-time chaotic dynamics. We define a Krylov-Ehrenfest time, which quantifies the eventual divergence of classical and quantum complexities, corresponding to a characteristic depth of the Krylov chain, $n\sim n_*(\hbar)$, which in the time domain translates to the well-known scale, $t_*\simλ_K^{-1}\log(1/\hbar)$, in generic chaotic systems. We additionally define microcanonical Krylov complexities, both in the classical and quantum setting, which allows one a fine-grained study of complexity, energy shell by energy shell. We apply this framework to the Lipkin-Meshkov-Glick (LMG) and Feingold-Peres (FP) models, which are collective spin systems known to classicalize in the thermodynamic limit. In particular, while the FP model features spectral chaos for some range of coupling values, the LMG model is known to exhibit early-time saddle-dominated scrambling. Our analysis shows that the instability in LMG is resolved by the microcanonical Krylov complexity, which is controlled by the integrable structure of the Hamiltonian in spectral windows away from the instability, both at early and late times.

We show that driven qubit junctions enable programmable control of physical entropy transport, with entropy conductance governed by quantum dynamics rather than by reservoir parameters alone. By comparing two simple quantum architectures -- a driven single-qubit junction and a driven two-qubit junction -- we find that the two-qubit junction enhances entropy transfer while requiring substantially lower driving power than its single-qubit counterpart. We further reveal two non-intuitive effects in both junctions: a sizable coherent contribution to the entropy current that emerges only under resonant driving, and negative differential entropy conductance, where increasing the thermal bias suppresses entropy flow into the probe reservoir. These results identify quantum logic architectures as programmable devices for entropy transport and suggest routes toward quantum feedback control, reservoir protection and refrigeration in driven quantum circuits.

Thermally charged quantum batteries provide a stable and easy-to-have energy resource, but remain passive and therefore do not allow useful energy extraction on demand via unitary operations. We introduce a time-dependent stirring protocol that activates a thermally charged quantum battery through a time-dependent coupling to an auxiliary activator, thereby generating correlations that drive the battery into an active state. Accounting explicitly for the energetic cost of the stirring process, the entropic cost of correlation generation and the activation time, we derive bounds on the maximal net extractable energy. Incorporating projective measurement on the activator and exploiting the information gained through the measurement further enhances the extractable energy. As an experimentally relevant example, we analyze a waveguide-QED setup where a harmonic-oscillator battery (waveguide) is stirred and monitored by a two-level system. We characterize the performance of the protocol in terms of net extractable energy (net ergotropy) and power output.

Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional strategies. However, most work remains confined to Markovian environments. When applied to non-Markovian systems, standard PINN architectures fail to converge reliably due to multi-objective optimization conflicts arising from the coupled differential equations. To address this fundamental limitation, we extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch. By decoupling the optimization objectives at the gradient level via selective gradient flow, our method turns a previously intractable multi-task optimization into a well-conditioned one, allowing simulation and control to be optimized jointly without compromise. Numerical simulations on a two-qubit Heisenberg XXX model confirm that our framework faithfully reproduces the features of non-Markovian dynamics, including decoherence and information backflow. Taking a state-preparation task on the same model as an example, our FPINN achieves higher fidelity than gradient ascent pulse engineering, chopped random basis, and standard PINNs, with the advantage becoming more pronounced as the environment becomes more dissipative and more Markovian. The generated pulses are also noticeably smoother, which is advantageous for experimental implementation. Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems, with potential implications for quantum computing, simulation, and control.

Large-scale Greenberger-Horne-Zeilinger~(GHZ) state is useful for quantum technologies but difficult to be prepared. Here, we propose fast measurement-based preparation of large-scale GHZ states by a four-qubit quantum phase gate with nuclear-spin qubits of alkaline-earth-like atoms, which is named as quantum ferromagnetic gate~(QFG) due to its analogy to the alignment of molecular magnetic moments in a classical magnet. A high-fidelity Rydberg-mediated QFG can be realized in a time of $6π/Ω_{\text{m}}$ with $Ω_{\text{m}}$ the maximal Rydberg Rabi frequency. From a product state of three data atom and one ancilla atom, a gluing circuit with one QFG, two single-qubit gates, and a projective measurement of the ancilla can generate a 3-qubit GHZ state, and repetition of this gluing circuit can lead to 9, 27, 81, 243, $\cdots$-qubit GHZ states. Analyses based on currently available techniques show that a 243-qubit GHZ state is realizable, and more qubits can be entangled with higher detection fidelity.

This manuscript investigates the possibility of defining a quantum Hamiltonian that leads to the same high-harmonic generation (HHG) spectra as those predicted by Floquet theory in the regime where the number of infrared (IR) pump photons is much larger than the number of emitted UV photons. The key assumption underlying our derivation is that the intensity of the emitted high harmonics is many orders of magnitude smaller than that of the IR laser, and that the emission of the Nnt harmonic results from the absorption of N IR photons.

This paper establishes a rigorous, unified geometric framework for quantum state spaces by constructing smooth, regular embeddings into higher-order dual number algebras $\mathcal{Q}_N \equiv \mathbb{R}[\varepsilon]/(\varepsilon^{N^2-1})$, wherein every quantum state is faithfully represented as a non-reduced scheme-theoretic point. We show that under this unified family of truncated rings, the non-linear matrix commutators governing the Liouville-von Neumann dynamics map globally onto flat, linear, and rigid algebraic flows, establishing nilpotent dual algebras as a pristine geometric landscape for higher-dimensional quantum kinematics.

Methanol radio lines observed in space provide sensitive probes of whether the proton-to-electron mass ratio has changed over cosmic time, but such tests require laboratory rest frequencies with very high accuracy. Here we determine the frequency of the astrophysically important 12.2 GHz $3_{-1}$E -- $2_{0}$E transition of CH$_3$OH by measuring near-infrared rovibrational transitions rather than the microwave line directly. Using wavelength-modulated NICE-OHMS locked to an ultra-stable optical frequency comb and referenced via a fiber link to a hydrogen-maser frequency standard, we measure Lamb-dip frequencies near 1.4 $μ$m (216 THz) with 10 Hz statistical reproducibility and absolute uncertainties as low as 130 Hz. Pairs of optical transitions sharing common upper levels form a triangulation scheme that yields the ground-state rotational combination difference. We obtain 12 178 596.415(135) kHz, improving on earlier molecular-beam microwave spectroscopy by a factor of 20 and agreeing with a recent free-induction-decay measurement. This result establishes a sub-kHz laboratory benchmark for a key radio-astronomical methanol line and demonstrates that optical triangulation can be extended to non-chiral molecules with internal rotation.

Quantum weakest preconditions are a fundamental tool for program verification of quantum programs. Many variations have been reported in the literature. We revisit quantum weakest preconditions from the perspective of expected runtime analysis of quantum programs and introduce a novel pre-expectation framework that enables to reason about the preconditions of quantum programs without the need of an upper bound. This is particularly interesting for quantum programs involving reward statements. The overall goal is to analyze runtime behavior even in the case of programs with potentially infinite expected runtime. This paper presents several ways to do so, e.g., a program transformation such that the expected runtime of a quantum program can be expressed using the weakest pre-expectation calculus with rewards.

Quantum nonlinear optics by Rydberg polaritons can enable single-photon transistor and switch, single-photon source, and deterministic quantum information processing. A major hindrance in this study is the fast motional decoherence. Here, we devise a scheme to significantly enhance the coherence of Rydberg polariton by letting the atoms {\it remember} their velocities, or, alternatively, by {\it changing} the phase of Rydberg polariton according to its storage time. After the Rydberg polariton is prepared with a Rydberg state $|r_1\rangle$, i.e., during the storage time, two laser fields induce a transition between $|r_1\rangle$ and a nearby Rydberg state $|r_2\rangle$ via a low-lying intermediate state $\lvert f\rangle$ which is largely detuned. In particular, we find that either a $2π\mathbb{N}$ protocol, a $π$-wait-$π$ protocol, or a wait-$π$ protocol, along with an appropriate choice of $\lvert f\rangle$ can lead to a phase-coherent Rydberg polariton upon its retrieval. Importantly, the coherent transition between $|r_1\rangle$ and $|r_2\rangle$ ensures that the Rydberg polariton can block the Rydberg excitation of nearby atoms as in usual applications of Rydberg polaritons. Numerics show that the theory can nearly completely eliminate the motional dephasing, leaving Rydberg-state decay as the only fundamental channel of decoherence. This sheds light on a broad application of Rydberg-mediated quantum nonlinear optics.

Many quantum machine learning models employ quantum feature maps to encode classical data into quantum states. While fixed feature maps often lack sufficient expressivity for complex nonlinear classification tasks, trainable quantum feature maps (TQFMs) enable adaptive quantum kernels with enhanced learning capability. Different loss functions can induce distinct optimization dynamics, yet their effects remain poorly understood. In this work, we apply the Log-Likelihood Loss function for TQFMs and provide a systematic comparison with Distance Loss and Measurement Loss. Through extensive numerical experiments, we compare their optimization dynamics, computational costs, and classification performance. Our results show that Log-Likelihood Loss consistently achieves more stable optimization than Measurement Loss while retaining linear computational complexity. The resulting benchmark offers practical guidance for balancing trainability, computational efficiency, and predictive performance in quantum kernel optimization.

Positive but not completely positive maps provide one of the most direct ways to detect entanglement beyond the positive-partial-transpose (PPT) criterion. We introduce and analyze an exactly solvable two-parameter family of sparse bistochastic positive maps on qutrits, in which two coherence channels are independently tuned by parameters $w$ and $z$. The sparse structure makes the full phase diagram analytic: positivity holds exactly on the square $0\le w,z\le2/3$, complete positivity on the smaller square $0\le w,z\le1/3$, and decomposability is lost precisely outside a quarter circle in the corner $w,z\ge1/3$. The indecomposable region is certified by explicit PPT entangled state adapted to the same witness geometry. At the endpoint $W_*=W(2/3,2/3)$ we construct a four-parameter family of PPT edge states of rank type $(5,5)$, derive their analytic detection region, and show that the corresponding rays are exposed faces of the PPT cone. Finally, although $W_*$ is not optimal, we give an explicit optimal refinement whose detection region on this family is strictly larger. The result is an analytically tractable qutrit setting in which positivity, indecomposability, PPT entanglement, optimality, and exposed convex geometry can be studied in a single framework.

The nitrogen-vacancy (NV) center in diamond can be operated as a microwave-dressed qubit. In the ideal two-level limit, its transition frequency is first-order insensitive to static magnetic fields, providing robustness against magnetic detuning noise. In practical diamond devices, however, residual transverse crystal strain mixes the $\ket{m_{s}=\pm1}$ spin sublevels and modifies the dressed qubit. In this study, we derive an analytical effective two-level model of a strained dressed NV qubit by perturbatively eliminating the far-detuned spectator state from the full three-level dressed Hamiltonian. We obtain closed-form expressions for the dressed-state splitting, the spin-locking mixing angle, and the longitudinal magnetic-field coupling. We show that transverse strain shifts the dressed-state resonance and tilts the spin-locking axis. These two effects restore a finite DC-field response and thereby quantify the loss of magnetic robustness. We demonstrate these features in simulated pulsed electron spin resonance spectra that incorporate rate-equation-based optical readout. We further derive exact validity criteria from the eigenvalues and spectator weights of the full three-level Hamiltonian. For practical use, we reduce these criteria to two controlled guidelines: the spectator-like branch must remain above the nominal upper dressed state, and its branch-specific admixture must remain small. A validity diagram over the axial-field--transverse-strain plane summarizes these approximate conditions and provides practical guidelines for designing dressed-NV sensing experiments.

We propose an efficient algorithm based on shadow Hamiltonian simulation to approximately simulate the real-time dynamics of observables under time-independent Hamiltonians. Shadow Hamiltonian simulation works at the level of the operator algebra generated by the observables through commutators with the Hamiltonian. Exactly encoding the quantum state in this picture is generally inefficient for interacting systems due to the exponential growth of the operator algebra. Our algorithm overcomes this bottleneck by systematically identifying the elements of the algebra most relevant to the target observables. This targeted approach is a controlled approximation that yields a highly efficient quantum state encoding that substantially reduces the size of the qubit register required to perform the time evolution using the shadow Hamiltonian. We propose two main pruning schemes, one based on a predefined operator basis and another on a constructed Krylov basis. We also present a hybrid scheme that builds a Krylov basis within a pruned algebra in the predefined basis. We benchmark our algorithm using lattice spin systems in one and two dimensions, for both one- and higher-point correlators as observables.

The memory kernel of an open quantum system obeys Kramers--Kronig (KK) relations if and only if its Laplace transform is analytic in the upper half-plane -- a property known as Hardy-space analyticity. Here we show that non-unitary exchange statistics, the defining property of paraparticles, intrinsically breaks Hardy-space analyticity. The metric $η$ that guarantees a real closed-system spectrum for these particles necessarily differs from the physical Born inner product ($\|η- I\|_F / \|I\|_F = 0.51$) -- a mathematical consequence of the R-matrix's non-unitarity, not a parameter choice. This metric is a "shadow metric": Schur's lemma forces it to commute with every bilinear observable, making the distortion physically invisible in the closed system. But when the paraparticle is coupled to a bath, any coupling operator that lies outside the symmetry algebra -- that is, any interaction that sees the internal flavour structure -- exposes the distortion. The memory kernel then develops upper-half-plane poles at coupling $g_c \approx 0.1$, breaking standard dispersion relations before the closed-system spectrum complexifies. Fermions and bosons, whose exchange is unitary ($η= I$ as an analytic fact of the canonical anticommutation algebra), are immune at any coupling, because there is no distortion to expose. The violation is intrinsic: it distinguishes non-unitary exchange statistics from ordinary particle statistics at the level of the memory kernel's analytic structure.

We investigate the resilience to noise of single-qubit quantum key distribution (QKD) protocols in the scenario of security against independent eavesdropping attacks and key distillation based on one-way classical communication. To this end, we introduce a noise-based metric that quantifies the efficiency of QKD protocols. Within this framework, we analyze the maximal noise levels that allow Alice and Bob to asymptotically establish a secure secret key. Using this assumption, we compare the noise tolerance of general single-qubit QKD protocols, in particular the BB84, B92, E91, and six-state protocols. Our main result determines the noise level threshold for QKD allowing one to distill an asymptotically secure secret key. Additionally, we demonstrate that the six-state protocol achieves the greatest resistance to noise while simultaneously yielding a higher post-selection efficiency than the other analyzed single-qubit protocols, confirming its robustness within the considered security model. Finally, we perform an analysis of the proposed noise-based metric and the conventional quantum bit error rate (QBER) metric.

Product formulas are among the most practical approaches to Hamiltonian simulation, requiring no ancillary qubits and exhibiting error bounds governed by nested commutators rather than only by Hamiltonian norms. Their circuit size, however, scales polynomially with the inverse precision. We develop a high-order nested-commutator compensation (HNCC) algorithm that preserves the main advantages of product formulas while achieving polylogarithmic precision dependence in the circuit size and the standard $\mathcal{O}(\varepsilon^{-2})$ sampling cost. HNCC uses a truncated Baker--Campbell--Hausdorff expansion to represent high-order Trotter errors by products of nested commutators and compensates these errors at the superoperator level through randomly sampled Pauli-rotation channels, avoiding Hadamard tests and ancillary qubits. For a fixed $K$-th order product formula applied to a $k$-local Hamiltonian on $N$ qubits with $Γ$ Pauli terms and local interaction strength $g_0$, HNCC estimates $\operatorname{tr}[Oe^{-\mathrm{i} tH}ρe^{\mathrm{i} tH}]$ to additive precision $\varepsilon\|O\|$ using $\mathcal{O}(\varepsilon^{-2})$ repetitions and a maximum gate count per circuit of $\mathcal{O}(N^{\frac{2}{2K+1}} (k g_0 t \log(1/\varepsilon))^{1+\frac{1}{2K+1}} k(Γ+\log(1/\varepsilon)))$. The resulting time dependence matches that of a product formula of order $2K+1$. Finite-size resource estimates for the periodic Heisenberg chain indicate that HNCC achieves the lowest CNOT and $T$-gate counts per circuit among the product-formula-based methods considered.

The Kirkwood-Dirac quasiprobability provides an operational representation of a quantum state, whose negativity serves as a measure of nonclassicality. Despite its fundamental importance, the extremal values of the Kirkwood-Dirac negativity are still unknown in the general case. We investigate the Kirkwood-Dirac quasiprobability of an arbitrary quantum state under Gaussian processes. In this setting, we derive an upper bound on the negativity for any number of modes and measurements. For a single mode and two measurements, we show that the eigenstates of the quadrature operators saturate this upper bound, while a nontrivial minimum is reached by pure Gaussian states. As a consequence, our results indicate that Gaussian states are sufficient to achieve extreme values of nonclassicality.

The sample complexity is the minimum number of copies required to learn an accurate classical description of a quantum state. Bosonic and fermionic Gaussian quantum states are families of quantum states that play a key role in quantum science and technology, from quantum optics and many-body physics to quantum chemistry, quantum computing, and quantum information theory. Despite their importance, their sample complexity had not been fully determined. We settle this open problem and show that both bosonic and fermionic Gaussian states can be learned using a number of copies that scales quadratically in the number of modes, regardless of whether the state is pure or mixed, and independently of any energy bound on the state. We derive these results by using the representation theory of Gaussian unitaries and by putting forth a generalization of the random purification channel to this setting and beyond.

Silicon spin qubits are a leading candidate for large-scale quantum computing owing to their compatibility with semiconductor manufacturing. However, scaling to useful fault-tolerant processors will likely generate thermal loads that exceed the cooling power available at millikelvin temperatures. Raising the operating temperature eases cooling requirements but reduces gate fidelity, increasing the overhead of quantum error correction. Identifying the operating temperature that minimizes total power consumption is therefore a key challenge for commercially viable quantum computers. Here, we use gate set tomography to benchmark two-qubit silicon chips fabricated in both industrial and academic environments over a range of temperatures. Elevated temperatures substantially shorten coherence times and increase gate and state-preparation-and-measurement infidelities. Based on these measurements, we develop a general power model for silicon quantum computers that combines cryogenic cooling requirements with error-correction overheads. We show that a finite optimal operating temperature exists and is strongly influenced by a crossover temperature near 1 K in current devices, above which gate fidelity degrades rapidly. These results connect device-level fidelity limitations to system-level power requirements, providing design guidelines for large-scale silicon quantum computers.

Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs. This fixed-trigger design is a major weakness because many defenses detect or weaken the repeated patterns such triggers leave in data representations. Although input-aware dynamic backdoors have been studied in classical neural networks, transferring them to QNNs is difficult because quantum learning introduces new obstacles. In particular, measurement compresses the post-ansatz quantum state into a limited classical output, weakening supervision for a trigger generator, while individual density matrices fluctuate with the input and make per-sample contrastive learning unstable. To address these challenges, we propose Q-DIBA, the first input-aware dynamic backdoor attack for QNNs. Q-DIBA jointly trains a classical trigger generator and a victim QNN through a three-mode mini-batch strategy that supports clean behavior, attack activation, and trigger specificity. To provide stable quantum-level supervision, Q-DIBA introduces an ensemble density contrastive loss that operates on post-ansatz quantum states before measurement and contrasts mode-averaged density matrices rather than individual samples. Experiments on MNIST and Fashion-MNIST across multiple QNN architectures show that Q-DIBA achieves high clean accuracy, strong attack success, and high cross-trigger accuracy, demonstrating effectiveness, stealthiness, and input specificity. The attack also remains resilient against defenses including visual inspection, spectral-signature detection, and fine-tuning, suggesting that input-aware quantum backdoors are an important threat to secure QNN deployment.

We investigate multiparameter quantum estimation in a molecular dimer composed of two dipole--dipole interacting two-level systems, focusing on the simultaneous estimation of temperature $T$ and detuning $Λ$. By employing a vectorization approach to derive the quantum Fisher information matrix, we analyze the precision limits of both simultaneous and individual estimation strategies. We show that simultaneous estimation outperforms the individual one in the near-resonant and low-temperature regime, where quantum coherence is enhanced, while its advantage is progressively reduced under detuned conditions and increasing temperature. Our results demonstrate that temperature acts as a key control parameter governing both estimation precision providing a unified perspective on quantum metrology. These findings highlight the potential of molecular quantum systems as realistic platforms for multiparameter quantum sensing.

Which properties of a quantum many-body system are operationally accessible is a central question underlying spectroscopy, thermodynamics, and quantum information science. Conventional response theory answers this question within a system-only paradigm: one perturbs and measures the matter itself, obtaining susceptibility built from causally ordered nested commutators. Here we show that coherently controlled quantum probes, when measured at the end, define a strictly larger operational learning framework beyond that accessible from response theory. We establish this through a quantum-circuit description that unifies spectroscopy, probe microscopy, and probe-based quantum technologies within a common operational framework, from which we develop quantum protocols for learning many-body properties from probe readout only. This advantage arises because the reduced dynamics of quantum probes generically encode anti-commutator and mixed-order correlators of the target; therefore, measurements on the probe provide access to fluctuations, non-equilibrium structure, and entanglement entropy that are in general not accessible through response functions or a single probe alone. Moreover, we demonstrate that entangled probes can access many-body properties such as von Neumann entropy. We prove that the required probe resources scale with the complexity of the target correlations rather than with the size of the many-body system. Quantum probes are therefore not merely more sensitive sensors but provide a new way to learn many-body properties distinct from those of tomography or quantum simulation.

We investigate multiparameter quantum estimation in a Raman-coupled two-qubit system at thermal equilibrium. Analytical expressions for the quantum Fisher information matrix are derived to characterize the simultaneous estimation of the temperature and Raman coupling strength. The corresponding quantum Cramér--Rao bounds are obtained and compared with those of individual estimation strategies. Our results reveal optimal operating regimes determined by the interplay between thermal fluctuations and coherent interactions. In particular, quantum thermometry exhibits a well-defined optimal temperature window, whereas the estimation of the Raman coupling strength is significantly enhanced in the low-temperature and weak-coupling regime. We further show that simultaneous estimation can outperform independent estimation within appropriate parameter regions, highlighting the advantages of multiparameter quantum metrology. These results provide analytical insights into the ultimate precision limits of Raman-coupled two-qubit systems and identify promising operating regimes for quantum sensing and quantum thermometry.

We investigate the private capacity of quantum channels using the recently proposed quantum convolution theory for discrete-variable quantum systems. We focus on the role of the magic resource played in this framework. Firstly, for a large class of convolutional channels, we find that the private capacity is zero if the fixed environmental state is a stabilizer state. Moreover, we show that the private capacity can be nonzero for some magic environmental states. Furthermore, we show that the private capacity of a discrete beam splitter unitary is upper-bounded by the amount of magic of the environmental state. In addition, if the environmental state exhibits a certain symmetric structure, even if it is magic, the corresponding private capacity will also vanish for a class of convolution. These results emphasize the role of magic resources in quantum communication

We discuss a new class of pure non-Gaussian quantum states of light characterized by trigonal symmetry on the phase plane. We propose the term ``trigonal states'' for them and show that they can be generated using the standard non-degenerate four-wave process supplemented by the subsequent heralding measurement of the photon number in one of the two signal modes.

Plasmonic nanostructures offer an effective route for enhancing light-matter interaction in atomically thin semiconductors, whose optical response is intrinsically limited by their sub-nanometer active thickness. Here, we numerically investigate excitonic enhancement in monolayer (ML) Molybdenum Disulfide (MoS2) coupled to size-tuned gold (Au) nanocube arrays separated by thin aluminum oxide (Al2O3) and hexagonal boron nitride (h-BN) spacer layers. By varying the nanocube side length, the localized surface plasmon resonance is tuned across the visible spectral range to modulate the A- and B-excitonic transitions of monolayer MoS2. We show that the nanocube-size-dependent spectral redshift can be further controlled through the spacer material and thickness, enabling systematic tuning of the near-field distribution, carrier generation rate, quantum yield, and radiative decay enhancement. Localized plasmonic confinement yields excitation-rate enhancements of up to 4.35 at B-excitonic transition (605 nm) and 3.66 at A-excitonic transition (650 nm), while the radiative decay-rate enhancement exceeds 80, leading to 350-fold photoluminescence enhancement. Although both A- and B-excitonic channels are enhanced simultaneously, their relative contributions depend on nanocube size, spacer material, and spacer thickness, indicating wavelength-dependent excitonic modulation rather than strict exciton-selective switching. These findings establish Au nanocube arrays as a simple, scalable, and tunable plasmonic platform for enhancing excitonic carrier generation and emission in ML MoS2.

Measurement incompatibility can remain intact at the operator level yet become operationally inaccessible when observations are restricted to the output of a quantum channel; we refer to this phenomenon as operational concealment. We develop a systematic adjoint-kernel framework for operational concealment in which observables are organized into operational equivalence classes determined by the kernel of the adjoint channel. This framework yields a structural classification of channels via kernel equivalence and monotonicity, together with a concealment robustness measure admitting explicit SDP formulations. It also yields an approximate concealment framework and a geometric characterization of concealment for unbiased binary qubit POVMs under rank-2 unital qubit channels. We show that concealment robustness coincides with standard incompatibility robustness for injective channels but can be strictly smaller for non-injective channels, as demonstrated by explicit analytical families. These results provide a systematic characterization and quantitative treatment of operationally inaccessible measurement incompatibility, with implications for restricted-access quantum information and semi-device-independent certification.

Covariance matrices lie at the heart of the powerful and well-established symplectic framework for describing continuous-variable Gaussian quantum states. However, since this framework only relies on first- and second-order moments, it is not sufficient for the analysis of non-Gaussian states because their higher-order moments are essential to capture some of their key properties. Here, we define higher-order covariance matrices -- more precisely, covariance matrices built from higher-order quadrature monomials -- which provide a simple way to evaluate the effect of Gaussian transformations on non-Gaussian states and can be used, for example, to address nonlinear squeezing or non-Gaussian nullifiers. Higher-order covariance matrices can be estimated from homodyne measurement data using only a limited number of quadrature angles, which involves matrices of moderate dimension compared with a full simulation in the Fock basis. The dimension of a higher-order covariance matrix does not depend on the span of the quantum states in Fock basis and, furthermore, scales only polynomially with the number of modes.

The advent of cryptographically relevant quantum computers poses an existential threat to classical public-key infrastructure. Quantum Key Distribution (QKD) addresses this challenge by providing information-theoretic security for key establishment, independently of any computational hardness assumption. In this work, the deployment and experimental validation of a metropolitan-scale quantum-secure network between data centers in Milan is reported. The network operates over installed fiber infrastructure and implements a layered architecture integrating QKD hardware, standards-compliant Key Management (KM), and centralized Software-Defined Networking (SDN) orchestration. Dynamic path reconfiguration via active optical switching and trusted-node routing allow automated fail-over solutions. Application-layer validation across diverse protocols and workloads confirms the seamless interoperability of all system components. These results establish the technical and operational readiness of metropolitan QKD networks for production deployment, and offer a replicable blueprint for building quantum-secure communication infrastructure at metropolitan scale.

Magnetic fields generally suppress phase-coherent Josephson transport, limiting superconducting interferometry to relatively low fields. Here we show that moiré-engineered graphene Josephson junctions can overcome this constraint. Using ballistic graphene/hBN junctions, we establish phase-coherent Andreev transport through Fabry-Pérot oscillations and Fraunhofer interference that persist across both the primary Dirac cone and reconstructed moiré minibands. We then demonstrate phase-coherent Josephson interference up to 6 T in the fractal Hofstadter-butterfly regime, well beyond the range expected for conventional ballistic graphene junctions. Comparison with Hofstadter-spectrum calculations reveals that superconductivity survives where the moiré potential transforms Landau levels with quenched group velocity into dispersive magnetic Bloch bands with finite quasiparticle group velocity, enabling extended electron-hole Andreev trajectories across the junction. Our results show that Hofstadter minibands can stabilize phase-coherent superconductivity deep into the parameter domain conventionally associated with the quantum Hall regime, establishing a new platform for high-field superconducting interferometry.

Multilevel dissipative systems can exploit multiple local transitions and coherence channels to generate nonstationary time-crystalline dynamics. Here we show that an analogous mechanism can be synthesized without enlarging the local Hilbert space by coupling two locally pumped and decaying spin-1/2 ensembles into a composite dissipative unit.Neither ensemble supports an autonomous oscillatory phase; instead, opposite pump-decay imbalances and inter-ensemble exchange coupling can lead to a single interlocked time crystal with a fixed internal phase relation and no single-ensemble counterpart. The time-crystalline character is consistently established through the mean-field analysis, exact calulation of Liouvillian spectra at finite size, and temporal correlations with cumulant expansion. Our work establishes a route to dissipative time-crystalline order in which coupling between simple two-level subsystems generates the effective internal structure otherwise provided by multilevel constituents.

Quantum computing has advanced rapidly in recent decades, driven by developments across the technology stack, including quantum error-correcting codes and efficient quantum algorithms. Among these, quantum arithmetic circuits serve as fundamental building blocks for various promising algorithms. Despite their crucial role, the design of quantum arithmetic circuits faces challenges arising from the no-cloning theorem, qubit limitations, and circuit depth constraints, which significantly impact the efficiency of large-scale quantum computing. We provide an overview of quantum arithmetic circuits in the context of public-key cryptanalysis, with particular emphasis on optimization strategies such as measurement-based uncomputation and conditionally clean ancilla. We review state-of-the-art designs for essential arithmetic operations in public-key cryptanalysis such as addition, multiplication, and modular exponentiation. We also present an overview of the techniques used for fault-tolerant runtime and resource estimation in quantum cryptanalysis. In brief, this chapter emphasizes strategies for designing resource-efficient quantum arithmetic circuits, providing a basis for realistic evaluations of quantum cryptanalytic capabilities.

Boson sampling demonstrates quantum advantage through the interference of indistinguishable particles, with output probabilities governed by matrix permanents. Realizing it on deterministic, matter-based platforms requires encoding the bosonic modes in finite-dimensional local Hilbert spaces, which introduces a leakage channel absent in linear optics: multi-particle bunching beyond the local truncation $d$. We develop a unified framework for non-interacting sampling on the irreducible representations of compact Lie groups, in which the transition amplitude is the immanant of a submatrix of the single-particle transition matrix, recovering the permanent in the bosonic case. Within this framework we bound the bunching leakage through a Dyson-series analysis: decomposing the correlated many-body leakage operator into independent random matrices and applying non-commutative concentration inequalities, we prove, in a Gaussian model of the transition matrix, that its spectral norm concentrates at $\tilde{O}(\sqrt{n})$ rather than the $O(n)$ worst-case of prior spin-based emulations; the passage to the physical Haar ensemble is reduced to a single submatrix-comparison input, verified at leading order. Exact numerics across local dimensions $d=2$--$5$ indicate that the bound is tight, the Haar-ensemble norm matching the closed form $\sqrt{d(n-d+1)}$ to sub-percent accuracy. This tightens the required mode number from $m=Ω(n^4)$ to the near-optimal $m=\tildeΩ(n^{1+2/(d-1)})$; for a spin-1 representation ($d=3$) the overhead falls to $m=\tildeΩ(n^2)$, matching the collision-free threshold. The result is independent of particle statistics and applies across finite-dimensional Lie-symmetric architectures, quantifying the spatial resources needed to preserve sampling hardness.

Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, heavily relied upon for predicting compound toxicity, bioavailability, and therapeutic potential. However, classical methods often struggle to effectively map the highly complex, non-linear, and high-dimensional interactions inherent in molecular data, leading to reduced predictive accuracy and costly late-stage clinical failures. In this paper, we present a Quantum Multiple Kernel Learning ($\mathtt{QMKL}$) framework, dubbed Next-Gen $\mathtt{Q^2SAR}$, that leverages Quantum Support Vector Machines ($\mathtt{QSVMs}$) to overcome these classical limitations. By encoding molecular descriptors into exponentially large quantum Hilbert spaces, our approach substantially enhances the expressiveness of non-linear modeling. Benchmarking our quantum-enhanced framework on a dataset targeting the $\mathtt{DYRK1A}$ kinase (a critical target for Alzheimer's disease), the $\mathtt{QMKL}$-$\mathtt{SVM}$ achieves an impressive Area Under the Curve ($\mathtt{AUC}$) score of $0.8750$, significantly outperforming classical state-of-the-art Gradient Boosting models ($\mathtt{AUC} = 0.8037$). Furthermore, we establish a theoretical and empirical pathway toward resolving classical data bottlenecks through projected quantum kernels ($\mathtt{PQK}$) and measurement accelerators. As quantum computing architecture matures, this framework paves the way for autonomous cognitive architectures and self-improving drug discovery pipelines, promising to unlock deeper insights across vast chemical spaces and to accelerate the development of life-saving therapeutics.

Reliable qubit readout is a critical bottleneck toward fault-tolerant quantum computing (FTQC). In superconducting quantum processors, readout operations are both error-prone and high-latency. These challenges become more severe in frequency-multiplexed architectures, where signal crosstalk among neighboring qubits significantly degrades readout fidelity. Existing machine learning (ML)-based approaches rely on feed-forward neural networks (FNNs) that suffer from large parameter sizes and lack an end-to-end network that jointly addresses relaxation errors and discriminates qubit states. In this work, we present a multi-stage qubit state discriminator based on the Mamba model, which enables efficient sequence modeling with linear complexity. The first stage performs initial state discrimination, followed by a refinement stage that identifies and mitigates relaxation-induced errors. Our lightweight model achieves a geometric mean readout fidelity of 0.906, outperforming the best-reported state-of-the-art method while reducing parameter size by 49.6%; our optimal model further reaches 0.911. Both models remain robust across varying input trace lengths, maintaining a high fidelity of 0.893 at readout durations as short as 500 $ns$, achieving up to a 26% reduction in logical error rate over prior work in quantum error correction (QEC).

Runtime assertions are a promising mechanism for testing and debugging quantum programs. But unlike the classical world, checking a quantum program that contains multiple assertions often requires using additional space or running the program additional times. For example, on current quantum hardware where mid-circuit measurement is restricted or costly, an assertion's pass/fail outcome cannot be revealed immediately. Instead, it is routed into an ancilla qubit during execution and read out by a terminal measurement. For a program with $n$ assertions, a naive strategy uses $n$ ancillas to learn all $n$ outcomes, while an alternative uses one ancilla but repeats program execution over $n$ rounds, checking one assertion per round. Both satisfy $S \cdot T = O(n)$, where $S$ is the number of ancillas and $T$ the number of executions: a fundamental time-space trade-off. Can one do asymptotically better? We reveal that the answer depends sharply on the information to be learned. Reporting the outcomes of all assertions requires linear complexity, but two partial-information tasks of detecting whether any assertion fails, and of identifying the first failing assertion, require only logarithmic complexity -- an asymptotic improvement. Moreover, the checking strategies for these tasks can trade time for space in useful ways. In this work, we formalize the complexity of checking multiple assertions in a quantum program. Using this definition, we establish its landscape of asymptotic lower bounds and constructive upper bounds. We confirm via a case study on Grover's algorithm that the resource costs of constructed strategies match theoretical predictions, illustrating the practical design space for quantum programmers.

A new phase diffusive regime in a current biased Josephson junction is theoretically explored which originates from embedding the junction in a circuit environment with anomalous dissipation. This is realized by placing parallel to the junction a resistor in series with a capacitor such that electromagnetic fluctuations effectively couple also to the charge of the junction. This leads to rich Josephson dynamics, in particular for the switching of the junction out of a zero voltage state. Modelled as the escape process of a fictitious phase-particle out of a metastable well, a detailed study reveals that anomalous dissipation has a strong impact at low temperatures when quantum tunneling dominates against thermal activation. As a manifestation, a regime is found, where for realistic circuit parameters the quantum escape process is substantially enhanced, followed by a short voltage pulse and re-trapping with high probability. This class of circuits may be leveraged for detecting microwave photons or dissipative quantum annealing processes. In addition, the analysis provides a general framework for engineering dissipative dynamics in nonlinear systems using anomalous environments.

We study timelike entanglement entropy and timelike subregion complexity in localized black holes with asymptotic AdS3*S3*T4 geometry, focusing on the black-pole solution. Unlike the BTZ solution, the black pole exhibits a nontrivial dependence on the internal sphere through the functions $K_y(r,θ)$ and $G(r,θ)$. Both observables are constructed from spacelike and timelike Lorentzian branches, but they probe the geometry in different ways: timelike entanglement yields a complex lifted area, while timelike complexity gives a real, finite renormalized volume. We employ a localized timelike prescription in which the branch profile is built at an angular label $θ_0$ and subsequently lifted over the physical internal angle $θ$. In the large-$r$ regime, the leading angular dependence drops out, recovering the expected short-interval behaviour. In the exact black-pole geometry, the temporal families become non-monotonic, making a fixed-boundary-interval selection essential. As the boundary interval increases, the selected branches move inward and become sensitive to the localized cap-horizon transition region. These results demonstrate that timelike Lorentzian observables probe localized-geometry effects that are absent in BTZ and in the leading large-$r$ description.

Despite rapid progress in quantum optimization, broad real-hardware benchmarks comparing multiple algorithmic families across diverse combinatorial problems under a common protocol remain limited. We benchmark gate-based quantum optimization on four NP-hard problems: multi-dimensional knapsack (MDKP), maximum independent set (MIS), quadratic assignment (QAP), and market-share (MSP). We study VQE, CVaR-VQE, standard, multi-angle, and warm-start QAOA, together with qubit-efficient PCE and QRAO, on IBM Heron r1/r2 processors using resilience-level-2 mitigation. To our knowledge, this includes the first real-hardware QRAO results and the first multi-problem PCE hardware benchmark. Across 247 method-instance combinations, we report transpiled circuit size, hardware outcomes, and an independent-error gate-count fidelity proxy, $F_{\mathrm{est}}$. For MDKP and MIS, an empirical operating point near $F_{\mathrm{est}}\approx 0.1$, corresponding to about 770 two-qubit gates at the median Heron-r2 CZ error rate, marks the onset of noise-dominated execution. QAP exposes a separate bottleneck: dense one-hot encodings and an exponentially sparse feasible manifold, with feasible fraction $10!/2^{100}$ at $n=10$; no tested hardware method produces a feasible assignment. Compiled QAOA-family circuits are generally noise dominated, and a matched uniform-random control shows that most feasible low-fidelity outcomes fall within the random range, apart from one finite-sample MIS warm-start exception. A SWAP-aware, fractional-gate, Nighthawk-topology compilation counterfactual reduces two-qubit counts but leaves all circuits below $F_{\mathrm{est}}=10^{-3}$. These conclusions apply to the tested implementations rather than QAOA in general. Qubit-efficient methods extend runnable instance sizes, but only within the empirical fidelity budget.

Block encoding is the standard technique for accessing matrix data in quantum linear-algebra algorithms. Its implementation directly affects its subnormalization, which in turn controls the algorithm's success probability, simulation time, and downstream costs. Explicit construction of block encodings with provably optimal subnormalization exists for only a handful of operators, with bespoke calculations used in its design. In this work, we develop a framework to block encode translation-invariant finite-difference operators on a periodic grid. These operators are the finite-difference discretizations of the constant-coefficient partial differential equations that sit at the core of scientific computing. We show that the moment order of these stencils can be used to simultaneously determine the continuum operator approximated, the vanishing order of the Fourier symbol, and the cost of the block encoding. From there, we derive a closed-form optimality criterion as a function of the stencil coefficients, which certifies whether the construction attains the optimal subnormalization for an entire operator family, uniformly in grid size, and quantifies the gap when it does not. The framework subsumes optimal constructions for the Laplacian operator in the literature and can be used to certify new instances at higher even orders, including the biharmonic operator. Furthermore, we derive success-probability floors parameterized by spectral properties of the operator's symbol and find explicit constants for the block encoding of the advection-diffusion family for which no prior explicit spatial block encoding exists.

We demonstrate how the interference effects for a quantum particle in the double-slit experiment can be described by classical probabilities. We investigate a classical field theory for a complex scalar field with probabilistic initial conditions. A central element are conserved charges leading to the concept of particles. These are statistical observables which describe properties of the probability distribution for field configurations. The conserved charges define subsystems for particle excitations of a vacuum state. We encode the probabilistic information for the one-particle subsystem in a complex wave function. The Liouville equation for the classical probability distribution implies that the time evolution of this wave function obeys the Schrödinger equation for a quantum particle in a potential. The potential arises from a space-dependence of the mass term in the otherwise relativistic classical field theory. It can be chosen arbitrarily, realizing the typical quantum effects of interference, tunneling or discrete energy spectra.

While a minimal Kitaev chain is promised to host unprotected Majorana zero modes, its role for spin qubits is relatively underappreciated. Following recent breakthroughs in the fine control of transport behaviors, we propose to use minimal Kitaev chain as a robust coupling module between spin qubits. Long-distance, anisotropic exchange coupling can be mediated by the Andreev bound states (ABSs) in the hybrid segment. The chemical potential of ABS gives a simple way to selectively control the coupling strength and its response to local perturbations. Moreover, this additional control degree of freedom creates a unique sweet spot, allowing both strong coupling and first-order immunity against charge noise. The protected qubit encoded on the minimal Kitaev chain at the sweet spot is shown to boast over 200 fold improvement in decoherence time.

Gate-based digital quantum simulations offer an exciting new paradigm for studying the many-body physics of strongly correlated systems. In this context, electron-phonon models are challenging for qubit-only quantum simulators, as bosonic degrees of freedom require costly finite-dimensional encodings. Here, we elaborate on an alternative approach based on a digital-analog circuit QED architecture, where fermions are encoded in transmon qubits while bosons are represented directly by microwave resonators. The central building block of this framework is a qubit-resonator Rabi gate that emulates strong electron-phonon coupling and can be implemented through a sequence of resonant Jaynes-Cummings gates interleaved with layers of single-qubit rotations. Using this Rabi gate as the fundamental unitary operation, we construct quantum circuits for the Hubbard-Holstein and Yukawa-Sachdev-Ye-Kitaev models, which describe, respectively, strongly correlated electrons coupled to phonons and phonon-mediated interactions among Majorana fermions. We further demonstrate how nonclassical phonon physics and signatures of quantum chaos in these models can be probed through circuit simulations, and develop measurement and variational protocols tailored to near-term superconducting quantum hardware.

The multi-qubit gates fidelity of superconducting quantum processors can be limited due to the dressing of computational states by noncomputational ones. Here, we experimentally and analytically investigate a transmon-transmon-bus system where the computational states dressing is tunable over a broad range. We estimate the dressing using three methods: a full three-element model, an effective mode approach, and an unperturbed mode approach. The obtained results highlight the importance of the accurate estimation and control of the computational states dressing in order to optimize gates on superconducting platform.

Deterministic sources of entangled photon pairs and indistinguishable photons are expected to play a key role in photonic quantum technologies. Semiconductor quantum dots are promising candidates due to their on-demand emission and compatibility with nanophotonic structures. However, current implementations face trade-offs between extraction efficiency, Purcell enhancement, as well as charge noise that causes blinking and degrades indistinguishability. Here we demonstrate a tunable nano-optoelectronic device based on a quantum dot embedded in a p-i-n diode circular-Bragg-grating-resonator and featuring extraction efficiencies up to 0.55(6) and Purcell-factor of $\sim$8. The device generates wavelength-tunable entangled photon pairs with suppressed blinking and raw (corrected) concurrence > 0.89 (0.91) over a range of 1.6 nm. The very same source also emits single, nearly Fourier-limited and highly indistinguishable photons with raw (corrected) $\mathcal{V}_{\text{HOM}}$ = 0.951(4) (0.988(6)). These results demonstrate a viable platform for semiconductor quantum photonics.

Neutral Atom Quantum Computing (NAQC) is an emerging modality for scalable quantum computation, valued for its long coherence times and the naturally identical atomic qubits. However, one of the main drawbacks is its slow execution rate, dominated by lengthy classical processing tasks, such as fluorescence imaging, cooling, and atom rearrangement. We address this bottleneck with AtomFlow, a field-programmable gate array (FPGA)-based control architecture that consolidates fluorescence-image analysis and a newly developed atom-rearrangement algorithm onto a single Zynq UltraScale+ device. By co-locating the two stages on the same board and emitting rearrangement moves in a streaming fashion as soon as they are computed, AtomFlow eliminates the round-trip latency of conventional host-mediated pipelines. Evaluated on a 16x16 atom array, AtomFlow achieves an end-to-end latency of 25.3 ms with a first-move latency of 4 ms and an average move generation of 1 ms. Furthermore, our scalability analysis demonstrates that the architecture can readily support larger atom arrays within a single-board resource budget.

We investigate discrete dynamics in quantum circuits with gates corresponding to the S-matrix of a supersymmetric 1+1D quantum field theory. We show that for a brick-wall configuration such circuits support both localized and delocalized dynamically conserved operators known as strong zero modes (SZM), the number of which depends on the parameter regime. We demonstrate that, while some of the SZM remain localized at boundaries, other SZM propagate ballistically, guided by a choice of circuit parameters. Such propagation can be explained by a strong Dzyaloshinskii Moriya term appearing in the dynamics. We describe how to exploit propagating SZM for quantum information transport and discuss the robustness to various types of noise.

We investigate the non-Hermitian skin effect (NHSE) and topological phases in a ladder model with staggered nonreciprocal inter-leg hopping, consisting of an Su-Schrieffer-Heeger (SSH) chain coupled to a normal tight-binding chain. By tuning the system parameters, we show that the direction of the NHSE under open boundary conditions can be reversed and can even become energy dependent. The NHSE is further characterized by the spectral winding number under periodic boundary conditions. We further demonstrate that the inter-leg coupling significantly enlarges the topologically nontrivial parameter regime. Remarkably, the zero-energy topological edge modes reside on different legs of the ladder and are characterized by real-space winding numbers $W=\pm1$. Furthermore, increasing the nonreciprocity of the inter-leg hopping modifies the topological phase boundaries and eventually drives the system into a topologically trivial phase. Our results establish staggered nonreciprocal inter-leg hopping as an effective mechanism for engineering both the non-Hermitian skin effect and topological phases in ladder systems.

Second-order boundary value problems are central to computational science, yet standard matrix-based numerical formulations can obscure the local geometric structure that quantum circuits may exploit. Here we introduce a framework for explicitly constructing finite-dimensional counterparts of continuous differential operators in a form compatible with quantum computation. Starting from the exterior derivative, its adjoint, and the Hodge operator, we derive discrete realizations of second-order operators on primal--dual cell complexes and reformulate them as star-local update rules expressed through explicit functions that return the relevant bounding chains rather than through matrix representations. This yields simple, uniform, and scalable quantum circuits. We demonstrate the construction for div--grad and curl--curl operators, showing that the same star-local compilation principle extends across different operators, cell complexes, and manifold dimensions within a common framework. More generally, the framework provides a systematic route to quantum algorithms for partial differential equations.

Quantum oracles are a common building block of many quantum algorithms, where circuit depth is a primary cost that directly affects overall performance. Synthesizing oracles for SAT (CNF) formulas under a limited ancilla budget, however, tends to yield deep circuits, as existing methods underexploit clause-level parallelism. In this work, we present the Clustered Synthesis Tree (CST), a depth-oriented framework whose core idea is to group the individual clause leaves of a hierarchical synthesis tree into clusters, exposing instance-dependent clause-level parallelism under ancilla constraints. CST comprises three parts: the clause-grouping problem it induces, which we formulate as an ancilla-constrained scheduling problem and prove NP-complete in general, is addressed by SeedGrow, a polynomial-time $O(m^2 k)$ heuristic; ClausePack, a reversible oracle that evaluates a cluster's clauses in parallel at only a logarithmic-depth overhead; and CST-Map, which compiles the clustered tree into an executable SAT-oracle. On random $4$-CNF under the same ancilla budgets, CST reduces the oracle's circuit depth over the state-of-the-art (SOTA) baseline by $68\%$--$94\%$. On the standard SATLIB benchmarks, CST achieves about a $2.6\times$--$43.2\times$ reduction over the SOTA baseline, with the largest gains under dense variable sharing, and matches the baseline's maximum-budget depth using only $3.7\%$--$20\%$ of its ancilla qubits. A Grover-search resource estimate shows the advantage carries over to the full algorithm, reducing total circuit depth by $70\%$--$89\%$.

Testing quantum states for symmetries has multiple applications in quantum state comparison or as a primitive in more complex quantum algorithms. We present a method that determines whether an input photonic state is invariant under the action of a cyclic group defined by an operator $\hat{S}$ with eigenvalues which are roots of unity. The results generalize previously known circle and SWAP tests related to suppression laws in Fourier interferometers and offer a new test for permutations defined by a binary shift and a way to search for eigenstates in multiphoton states. Finally, we discuss the scenarios where this measurement can be used to perform Hadamard test, a fundamental primitive in variational and quantum machine-learning algorithms.

We investigate the use of variational quantum algorithms to prepare and characterize fractional quantum Hall states on near-term quantum processors. Focusing on the $ν=1/3$ Laughlin phase described by the $V_1$ Haldane pseudopotential, we formulate the lowest-Landau-level problem in second quantization, and implement particle-number-preserving variational circuits combined with the variational quantum eigensolver (VQE) and variational quantum deflation (VQD). We benchmark the approach in two complementary geometries: Haldane sphere and torus shape. On the Haldane sphere, the target state is a unique zero-energy Laughlin ground state, providing a controlled test of the variational workflow and of excited-state reconstruction. On the torus, the problem retains the genuinely two-dimensional periodic character of the quantum Hall liquid and exhibits the threefold topological ground-state degeneracy expected for the $ν=1/3$ fractional filling factor. This feature makes the torus a more demanding benchmark than the quasi-one-dimensional cylinder or thin-torus limits commonly exploited in state-preparation quantum protocols. We benchmark the hardware-optimized variational states against exact diagonalization using energy estimates, error-mitigated observables, and subspace-containment diagnostics. Our results show that hybrid quantum algorithms can approximately reconstruct the low-energy structure of small fractional quantum Hall systems, including the topological ground-state manifold on the torus. Beyond serving as a benchmark for quantum hardware, this geometry-resolved approach provides a route toward quantum simulations of fractional Chern insulators and strongly correlated topological phases in realistic two-dimensional materials.

Moment-based relaxations of the positive partial transpose (PPT) criterion have been recently introduced, as a hierarchy of entanglement criteria involving only experimentally accessible quantities of a given bipartite state. The goal of this work is to study their typical detection performance on high-dimensional bipartite systems. Concretely, we investigate whether random bipartite mixed states on $\mathbb C^d\otimes\mathbb C^d$, obtained as the marginal over an environment $\mathbb C^s$ of a uniformly distributed pure state, generically satisfy or violate them. For each fixed level $m\in\mathbb N$ in this hierarchy of moment-based PPT criteria, we are able to identify a threshold environment dimension $s=λ_md^2$ at which the behavior of the associated random state switches from violating to satisfying it, with probability going to $1$ as $d$ grows. The proof combines combinatorics of permutations techniques to estimate the average value of moments of partially transposed random states and concentration of measure arguments to bound the probability of deviating from such average, when the underlying local dimension $d$ is large. We additionally need tools from the theory of Hankel determinant evaluation via orthogonal polynomials.

We control the spectral distribution of a spin ensemble by applying a magnetic field gradient using an anti-Helmholtz coil inside a dilution refrigerator, and demonstrate impedance matching between the ensemble and a transmission line, achieving -50 dB absorption of incident radiation. This represents a first step toward a spin-ensemble-based quantum memory for itinerant microwave photons. We further model the spectral distribution under the applied gradient to predict the spin-resonator response, and use the device to systematically tune the weak-to-strong coupling transition in both continuous-wave and time-domain pulsed measurements.

Privacy of a quantum metrological protocol concerns the extent to which single parameters can be kept inaccessible to an observer or to other users of the network. In this work, an information geometric framework is developed to quantify privacy and accessibility of functions of parameters effectively, that is, up to a finite accuracy in state discrimination. Both quantities are defined by measuring volumes in the parameter space induced by the underlying quantum states. This construction subsumes previous definitions of privacy based on the degeneracy of quantum Fisher information, naturally encompassing imperfect implementations. Using extended-GHZ states as a representative example of a quantum network scenario, privacy and accessibility are characterized by quantum correlations and accuracy, providing scaling laws depending on imperfect measurements and entanglement.

Weak values (WVs) arise from weak measurements performed within a time-symmetric formulation of quantum mechanics, where a system is both pre- and post-selected. Anomalous WVs that lie far outside the eigenvalue spectrum of the observable hold both fundamental and practical significance. However, their generation typically relies on near-orthogonal pre- and post-selection, which confines them to a single post-selection outcome with extremely low success probability. This constraint limits experimental accessibility and hinders the full exploitation of time symmetry. To overcome these limitations, we utilize quantum entanglement and post-selection-controlled operations to generate correlated pairwise two-state vectors. By changing the role of post-selection from passive filtering to active engineering, this approach enables the observation of anomalous complementary WVs associated with mutually exclusive post-selection branches. Our results extend the operational accessibility of time-symmetric quantum structures associated with the two-state vector formalism, and open new avenues for exploring the applications of time symmetry in quantum information processing.

The short Doppler-limited coherence time of the laser-excited Rydberg state, usually orders of magnitude shorter than the lifetime of the Rydberg state, hinders the Rydberg-mediated quantum technologies. Here, we show that a 649~nm~$-$~658~nm two-photon excitation of the $6sng~^1G_4$ Rydberg state from a long-lived d-orbital clock state of barium can be achieved with a two-photon wavevector that is tiny, which effectively removes the Doppler-limited decoherence. Moreover, the $6sng~^1G_4$ Rydberg state has strong dipole-dipole interaction due to small Förster defect with nearby Rydberg states and possesses long radiative lifetime. These can benefit quantum computing based on individually trapped neutral atoms, and can enable long-lived Rydberg polaritons in atomic media, which brings fresh opportunities in all-optical quantum information.

Quantum state tomography is sample-starved, and the states one prepares live on a narrow, learnable manifold. A $k{=}0$ prior-only control shows that on concentrated families a prior estimate is already near-optimal, so ``high fidelity at few measurements'' can be family memorization rather than tomography; genuine measurement-efficiency needs a model that conditions on the measurements and demonstrably uses them. On a shared matrix-product-state (MPS) core parameterization we study two routes. Approach~A learns a generative prior over MPS cores with measurement-guided posterior inference (gold-standard-validated, but whose few-measurement accuracy the control shows is largely the prior). Approach~B, our main proposal, is a \emph{fixed-protocol amortized} MPS estimator trained once with a gauge-invariant fidelity loss; we deliberately do not rest it on a permutation-invariant set encoder (a plain MLP matches it). The decisive lever is the measurement design: motivated by the fact that local reduced density matrices determine a $χ$-MPS, conditioning on an \emph{informative local} Pauli set rather than random strings turns a modest, memorization-prone estimator into a high-fidelity one ($\approx\!0.95$, up to $+0.59$ over prior-only, decisively passing a shuffled-measurement control). A dropout ensemble, conformally recalibrated, gives $\approx\!90\%$-coverage intervals -- including for observables never measured, where a shot-based interval does not exist. Quality holds as the system grows (fidelity $0.90$ at $n{=}10$, gain \emph{growing} in $n$; $0.88$ at bond dimension $χ{=}4$), the parameterization is polynomial (native contraction to $20$ qubits), and we close the loop on IBM hardware ($5$ states at $0.97$ from hardware-measured Paulis).