Quantum Physics Paper Analysis

We introduce group surface codes, which are a natural generalization of the $\mathbb{Z}_2$ surface code, and equivalent to quantum double models of finite groups with specific boundary conditions. We show that group surface codes can be leveraged to perform non-Clifford gates in $\mathbb{Z}_2$ surface codes, thus enabling universal computation with well-established means of performing logical Clifford gates. Moreover, for suitably chosen groups, we demonstrate that arbitrary reversible classical gates can be implemented transversally in the group surface code. We present the logical operations in terms of a set of elementary logical operations, which include transversal logical gates, a means of transferring encoded information into and out of group surface codes, and preparation and readout. By composing these elementary operations, we implement a wide variety of logical gates and provide a unified perspective on recent constructions in the literature for sliding group surface codes and preparing magic states. We furthermore use tensor networks inspired by ZX-calculus to construct spacetime implementations of the elementary operations. This spacetime perspective also allows us to establish explicit correspondences with topological gauge theories. Our work extends recent efforts in performing universal quantum computation in topological orders without the braiding of anyons, and shows how certain group surface codes allow us to bypass the restrictions set by the Bravyi-K{ö}nig theorem, which limits the computational power of topological Pauli stabilizer models.

The realization of quantum error correction protocols whose logical error rates are suppressed far below physical error rates relies on an intricate combination: the error-correcting code's efficiency, the syndrome extraction circuit's fault tolerance and overhead, the decoder's quality, and the device's constraints, such as physical qubit count and connectivity. This work makes two contributions towards error-corrected quantum devices. First, we introduce mirror codes, a simple yet flexible construction of LDPC stabilizer codes parameterized by a group $G$ and two subsets of $G$ whose total size bounds the check weight. These codes contain all abelian two-block group algebra codes, such as bivariate bicycle (BB) codes. At the same time, they are manifestly not CSS in general, thus deviating substantially from most prior constructions. Fixing a check weight of 6, we find $[[ 60, 4, 10 ]], [[ 36, 6, 6 ]], [[ 48, 8, 6 ]]$, and $[[ 85, 8, 9 ]]$ codes, all of which are not CSS; we also find several weight-7 codes with $kd > n$. Next, we construct syndrome extraction circuits that trade overhead for provable fault tolerance. These circuits use 1-2, 3, and 6 ancillae per check, and respectively are partially fault-tolerant (FT), provably FT on weight-6 CSS codes, and provably FT on \emph{all} weight-6 stabilizer codes. Using our constructions, we perform end-to-end quantum memory experiments on several representative mirror codes under circuit-level noise. We achieve an error pseudothreshold on the order of $0.2\%$, approximately matching that of the $[[ 144, 12, 12 ]]$ BB code under the same model. These findings position mirror codes as a versatile candidate for fault-tolerant quantum memory, especially on smaller-scale devices in the near term.

We study the decoding problem for quantum Tanner codes and propose to exploit the underlying local code structure by grouping check nodes into more powerful generalized check nodes for enhanced iterative belief propagation (BP) decoding by decoding the generalized checks using a maximum a posteriori (MAP) decoder as part of the check node processing of each decoding iteration. We mainly study the finite-length setting and show that the proposed enhanced generalized BP decoder for quantum Tanner codes significantly outperforms the standard quaternary BP decoder with memory effects, as well as the recently proposed Relay-BP decoder, even outperforming generalized bicycle (GB) codes with comparable parameters in some cases. For other classes of quantum low-density parity-check (qLDPC) codes, we propose a greedy algorithm to combine checks for generalized BP decoding. However, for GB codes, bivariate bicycle codes, hypergraph product codes, and lifted-product codes, there seems to be limited gain by combining simple checks into more powerful ones. To back up our findings, we also provide a theoretical cycle analysis for the considered qLDPC codes.

We present a fast and effective framework for analysing and designing syndrome-extraction circuits (SECs). Our approach is based on left-right circuits, a general design for SECs which maintain low depth by staggering $X$ and $Z$ checks without interleaving gates. Initially proposed for specific classes of codes, we generalise this construction to arbitrary CSS codes and optimise the circuit structure to achieve low qubit idling time, large effective distance, and reduced minimum-weight failure mechanisms. A key component of our framework is the formal notion of residual errors and their associated distance metrics, which form lightweight tools for capturing error propagation and quantifying the potential harm of circuit-level errors. Applying our automated framework to diverse classes of codes, we observe consistent improvements in logical performance of up to an order of magnitude compared to existing single-ancilla SEC designs. We also use these tools to prove that no non-interleaving SEC can achieve circuit distance $12$ for the gross code, and identify an explicit circuit that we conjecture achieves distance $11$, exceeding previously known constructions.

Amplitude estimation, in its original form, is formulated as phase estimation upon the Grover walk operator. Since its introduction, subsequent improvements to the algorithm have removed the use of phase estimation and introduced low-depth variants that trade speedup factors for lower circuit depth. We make the key observation that amplitude estimation is equivalent to estimating the energy gap of an effective Hamiltonian, whereby discrete time evolution is generated by amplitude amplification. This enables us to develop two amplitude estimation algorithms for both Heisenberg-limited and low-depth circuit regimes, inspired by statistical phase estimation techniques developed for seemingly unrelated early fault-tolerant ground-state energy estimation. Our approach has significant technical and practical benefits, and uses simplified classical post-processing compared to prior techniques -- our theoretical and numerical results indicate that we achieve state-of-the-art performance. Furthermore, while our approach achieves Heisenberg-limited scaling, we also establish optimal query-depth tradeoffs up to polylogarithmic factors in the low-depth regime with provable theoretical guarantees. Due to its flexibility, generality, and robustness, we expect our approach to be a key enabler for a broad range of early fault-tolerant applications.

Correlated noise is a critical failure mode in quantum error correction (QEC), as temporal memory and spatial structure concentrate faults into error bursts that undermine standard threshold assumptions. Yet, a fundamental gap persists between the stochastic Pauli models ubiquitous in QEC and the microscopic, non-Markovian descriptions of physical device dynamics. We close this gap by introducing \emph{Spatiotemporal Pauli Processes} (SPPs). By applying a multi-time Pauli twirl -- operationally realised by Pauli-frame randomisation -- to a general process tensor, we map arbitrary multi-time, non-Markovian dynamics to a multi-time Pauli process. This process is represented by a process-separable comb, or equivalently, a well-defined joint probability distribution over Pauli trajectories in spacetime. We show that SPPs inherit efficient tensor network representations whose bond dimensions are bounded by the environment's Liouville-space dimension. To interpret these structures, we develop transfer operator diagnostics linking spectra to correlation decay, and exact hidden Markov representations for suitable classes of SPPs. We demonstrate the framework via surface code memory and stability simulations of up to distance \(19\) for (i) a temporally correlated ``storm'' model that tunes correlation length at fixed marginal error rates, and (ii) a genuinely spatiotemporal 2D quantum cellular automaton bath that maps exactly to a nonlinear probabilistic cellular automaton under twirling. Tuning coherent bath interactions drives the system into a pseudo-critical regime, exhibiting critical slowing down and macroscopic error avalanches that cause a complete breakdown of surface code distance scaling. Together, these results justify SPPs as an operationally grounded, scalable toolkit for modelling, diagnosing, and benchmarking correlated noise in QEC.

An algorithm for the generation of shuttling sequences is necessary for the operation of a linear segmented ion-trap quantum computer. The present work provides an implementation of an algorithm that produces sequences proved to be optimal for circuits with a quantum Fourier transform-like structure. Such optimality was proved in previous work of our group. We first present an approach for qubit mapping, i.e. determining the initial ordering of the ions, termed the common ion order, and develop a heuristic algorithm for its implementation. We explain how this heuristic is integrated in the shuttling sequence generation algorithm described in the previous work. The results show the increased performance of the heuristic in terms of reducing the number of required shuttling operations. The number of ion displacements required exhibits a polynomial increase in terms of the number of qubits, such that these operations become the main contribution to the overall resource cost. Furthermore, we show that multiple zones for gate interactions can reduce the amount of qubit register reordering.

Magic states are a scarce resource for two-dimensional qubit stabilizer codes. Magic state cultivation was recently proposed to reduce the cost of magic state preparation by measuring the transversal Clifford operator of the color code. Cultivation achieves $\sim 10^{-9}$ logical error rates for the $d=5$ color code, with substantially lower space-time overhead than magic state distillation. However, due to the $\mathcal{O}(d)$ depth of the Clifford measurement circuit, magic state cultivation becomes impractical for $d>5$. Here, we perform logical $XS^\dagger$ measurements on the color code by gauging a transversal Clifford gate, resulting in a constant-depth logical measurement circuit. We employ repeated gauging measurements with post-selection rather than performing error correction on the Clifford stabilizer code that emerges during the gauging protocol, thus gaining simplicity at the cost of scalability. Our protocol requires a regular square grid connectivity and yields logical error rates comparable to magic state cultivation. The $d=7$ version of our protocol gives access to the $10^{-12}$ logical error rate regime at $0.05\%$ physical error rate while retaining more than $1\%$ of the shots after the equivalent of the cultivation stage.

We propose a new decoder for ``matchable'' qLDPC codes that uses a Markov-Chain Monte-Carlo algorithm -- called the \emph{worm algorithm} -- to approximately compute the probabilities of logical error classes given a syndrome. The algorithm hence performs (approximate) \emph{optimal} decoding, and we expect it to be computationally efficient in certain settings. The algorithm is applicable to decoding random errors for the surface code, the honeycomb Floquet code, and hyperbolic surface codes with constant rate, in all cases with and without measurement errors. The efficiency of the decoder hinges on the mixing time of the underlying Markov chain. We give a rigorous mixing time guarantee in terms of a quantity that we call the \emph{defect susceptibility}. We connect this quantity to the notion of disorder operators in statistical mechanics and use this to argue (non-rigorously) that the algorithm is efficient for \emph{typical} errors in the entire decodable phase. We also demonstrate the effectiveness of the worm decoder numerically by applying it to the surface code with measurement errors as well as a family of hyperbolic surface codes. For most codes, the matchability condition restricts direct application of our decoder to noise models with independent bit-flip, phase-flip, and measurement errors. However, our decoder returns \emph{soft information} which makes it useful also in heuristic ``correlated decoding'' schemes which work beyond this simple setting. We demonstrate this by simulating decoding of the surface code under depolarizing noise, and we find that the threshold for ``correlated worm decoding'' is substantially higher than for both minimum-weight perfect matching and for correlated matching.

Cross-entropy benchmarking (XEB) with single-qubit reference sequences is widely used to characterize multi-qubit gates in large-scale quantum processors, despite the lack of a rigorous theoretical justification. Here we show that the commonly employed additive single-qubit errors approximation underlying this approach breaks down and leads to a systematic overestimation of gate fidelities. We derive an analytical expression for the joint decay of simultaneous single-qubit reference sequences and introduce a refined expression for the interleaved gate fidelity estimation. Experiments on a superconducting quantum processor validate the theory and demonstrate that fidelities obtained using XEB with single-qubit references agree with those extracted from standard interleaved randomized benchmarking (IRB), while achieving higher precision due to reduced reference-sequence errors. Our results establish theoretical foundation for the single-qubit-based XEB and show that, with appropriate post-processing, it enables a reliable and robust approach for entangling gates benchmarking without the need for multi-qubit Clifford reference sequences.

I reduce the cost to prepare magic states with lattice surgery operations on the surface code by using a recursive implementation of 15-to-1 magic state distillation. On a rotated surface code with distance $d$, $|T\rangle$ preparation requires a $d$-by-$3 d$ grid of data qubits for up to $15 d$ error correction cycles, and $|CCZ\rangle$ preparation requires a $3 d$-by-$2 d$ grid for up to $10.5 d$ cycles. However, a significantly lower physical error threshold than that of the underlying surface code is required to match the error probability of the output magic state with the logical error rate of the output surface code at large code distances.

Two-dimensional topological translationally-invariant (TTI) quantum codes, such as the toric code (TC) and bivariate bicycle (BB) codes, are promising candidates for fault-tolerant quantum computation. For such codes to be practically relevant, their decoders must successfully correct the most likely errors while remaining computationally efficient. For the TC, graph-matching decoders satisfy both requirements and, additionally, admit provable performance guarantees. Given the equivalence between TTI codes and (multiple copies of) the TC, one may then ask whether TTI codes also admit analogous graph-matching decoders. In this work, we develop a graph-matching approach to decoding general TTI codes. Intuitively, our approach coarse-grains the TTI code to obtain an effective description of the syndrome in terms of TC excitations, which can then be removed using graph-matching techniques. We prove that our decoders correct errors of weight up to a constant fraction of the code distance and achieve non-zero code-capacity thresholds. We further numerically study a variant optimized for practically relevant BB codes and observe performance comparable to that of the belief propagation with ordered statistics decoder. Our results indicate that graph-matching decoders are a viable approach to decoding BB codes and other TTI codes.

Quantum error correction is critical to the design and manufacture of scalable quantum computing systems. Recently, there has been growing interest in quantum low-density parity-check codes as a resource-efficient alternative to surface codes. Their adoption is hindered by the difficulty of compiling fault-tolerant logical operations. A key challenge is that logical qubits do not necessarily map to disjoint sets of physical qubits, which limits parallelism. We introduce clustered-cyclic codes, a quantum low-density parity-check code family with finite-size instances such as [[136,8,14]] and [[198,18,10]] that are competitive with state-of-the-art constructions. These codes admit a directly addressable logical basis, enabling highly parallel logical measurement layers. To leverage this structure, we propose parallel product surgery for quantum product codes. Using an auxiliary copy of the data patch and an engineered product-connection structure, the protocol performs many logical Pauli-product measurements in a single surgery round with small, fixed overhead. For clustered-cyclic codes, this yields surface-code-style maximal parallelism: up to k/2 disjoint Pauli-product measurements per round under explicit algebraic conditions. We prove that parallel product surgery preserves the code distance for hypergraph product codes and numerically verify distance preservation for the listed clustered-cyclic instances with k = 8. Finally, for the [[24,8,3]] clustered-cyclic code, treating half of the logical qubits as auxiliaries enables arbitrary parallel CNOTs on disjoint pairs; combined with symmetry-derived operations, these gates generate the full Clifford group fault-tolerantly.

The ability to fault-tolerantly prepare CAT states, also known as multi-qubit GHZ states, is an important primitive for quantum error correction. It is required for Shor-style syndrome extraction, and can also be used as a subroutine for doing fault-tolerant state preparation of CSS codewords. Existing approaches to fault-tolerant CAT state preparations have been found using computationally expensive heuristics involving SAT solving, reinforcement learning, or exhaustive analysis. In this paper, we constructively find optimal circuits for CAT states in a more scalable way. In particular, we derive formal lower bounds on the number of CNOT gates required for circuits implementing $n$-qubit CAT states that do not spread errors of weight at most $t$ for $1\leq t \leq 5$. We do this by using fault-equivalent rewrites of ZX-diagrams to reduce it to a problem of characterising certain 3-regular simple graphs. We then provide families of such optimal graphs for infinitely many values of $n$ and $t\leq5$. By encoding the construction of optimal graphs as a constraint satisfaction problem we find explicit constructions for circuits that match this lower bound on CNOT count for all $n\leq50$ and $t \leq 5$ and for nearly all pairs $(n,t)$ with $n\leq 100$ and $t\leq 5$ or $n\leq 50$ and $t\leq 7$, significantly extending the regimes that were achievable by previous methods and improving the resource counts for existing constructions. We additionally show how to trade CNOT count against depth, allowing us to construct constant-depth fault-tolerant implementations using $O(n)$ ancilla and $O(n)$ CNOT gates.

The usual belief propagation (BP) decoders are, in general, exchanging local information on the Tanner graph of the quantum error-correcting (QEC) code and, in particular, are known to not have a threshold for the surface code. We propose novel BP decoders that exchange messages on the decoding graph and obtain code capacity thresholds via standalone BP for the surface code under depolarizing noise. Our approach, similarly to the minimum weight perfect matching (MWPM) decoder, is applicable to any graphlike QEC code. The thresholds observed with our decoders are close to those obtained by MWPM. This result opens the path towards scalable hardware-accelerated implementations of MWPM-compatible decoders.

Quantum error correction will likely be essential for building a large-scale quantum computer, but it comes with significant requirements at the level of classical control software. In particular, a quantum error-correcting code must be supplemented with a fast and accurate classical decoding algorithm. Standard techniques for measuring the parity-check operators of a quantum error-correcting code involve repeated measurements, which both increases the amount of data that needs to be processed by the decoder, and changes the nature of the decoding problem. Knill error correction is a technique that replaces repeated syndrome measurements with a single round of measurements, but requires an auxiliary logical Bell state. Here, we provide a theoretical and numerical investigation into Knill error correction from the perspective of decoding. We give a self-contained description of the protocol, prove its fault tolerance under locally decaying (circuit-level) noise, and numerically benchmark its performance for quantum low-density parity-check codes. We show analytically and numerically that the time-constrained decoding problem for Knill error correction can be solved using the same decoder used for the simpler code-capacity noise model, illustrating that Knill error correction may alleviate the stringent requirements on classical control required for building a large-scale quantum computer.

Recent advancements in quantum technologies have highlighted the importance of mitigating system imperfections, including parameter uncertainties and decoherence effects, to improve the performance of experimental platforms. However, most of the previous efforts in quantum control are devoted to the realization of arbitrary unitary operations in a closed quantum system. Here, we improve the algorithm that suppresses system imperfections and noises, providing notably enhanced scalability for robust and optimal control of open quantum systems. Through experimental validation in a superconducting quantum circuit, we demonstrate that our approach outperforms its conventional counterpart for closed quantum systems with an ultra-low infidelity of about $0.60\%$, while the complexity of this algorithm exhibits the same scaling, with only a modest increase in the prefactor. This work represents a notable advancement in quantum optimal control techniques, paving the way for realizing quantum-enhanced technologies in practical applications.

Recent progress in fault-tolerant quantum computing suggests that leveraging error-syndrome information at the logical layer can substantially improve performance, including the estimation of logical observables from noisy states. In this work, based on quantum estimation theory, we develop an information-theoretic framework to quantify the utility of error syndromes for noisy logical observable estimation. We distinguish two operational regimes of such syndrome-aware protocols: classical protocols, in which the logical measurement basis is fixed and syndrome information is used only in classical post-processing, and quantum protocols, in which the logical quantum control can be tailored to depend on the observed error syndrome. For classical syndrome-aware protocols, we prove a universal limitation: on average, syndrome information can improve the effective logical error rate by at most a factor of two, implying at most a quadratic reduction in sampling overhead. In contrast, once syndrome-conditioned quantum control is permitted, we exhibit settings in which the effective logical error rate decays exponentially with the number of logical qubits. These findings provide fundamental guidance for designing future fault-tolerant architectures that actively exploit syndrome records rather than discarding them after decoding.

Quantum code surgery offers a flexible, low-overhead framework for executing logical measurements within quantum error-correcting codes. It encompasses several fault-tolerant logical computation schemes, including parallel surgery, universal adapters and fast surgery, and serves as the key primitive in extractor architectures. The efficiency of these schemes crucially depends on constructing low-overhead ancilla systems for measuring arbitrary logical operators in general quantum Low-Density Parity-Check (qLDPC) codes. In this work, we introduce a method to construct an ancilla system of qubit size $O(W \log W)$ to measure an arbitrary logical Pauli operator of weight $W$ in any qLDPC stabilizer code. This new construction immediately reduces the asymptotic overhead across various quantum code surgery schemes.

Quantum weight reduction procedures ease the implementation of quantum codes by sparsifying them, resulting in low-weight checks and low-degree qubits. However, to date, only few quantum weight reduction methods have been explored. In this work we introduce a simple and general procedure for quantum weight reduction that achieves check weight 6 and total qubit degree 6, lower than existing procedures at the cost of a potentially larger qubit overhead. Our quantum weight reduction procedure replaces each qubit and check in an arbitrary Calderbank-Shor-Steane code with an ample patch of surface code, these patches are then joined together to form a geometrically nonlocal Layer Code. This is a quantum analog of the simple classical weight reduction procedure where each bit and check is replaced by a repetition code. Due to the simplicity of our weight reduction procedure, bounds on the weight and degree of the resulting code follow directly from the Layer Code construction and hence are easily verified by inspection. Our procedure is well suited for implementation in modular architectures that consist of surface code patches networked via long-range interconnects.

Hybrid continuous-variable (CV)-discrete-variable (DV) quantum systems present a promising direction for quantum computing by combining the high dimensional encoding capabilities of qumodes with the control offered by DV qubits on the coupled qumodes. There have been exciting recent progresses on hybrid CV-DV quantum computing, including variational algorithms, error correction, compiler-level optimizations for Hamiltonian simulation, etc. However, there is a lack of a standardized CV-DV benchmark suite for assessing various emerging hardware platforms and evaluating software optimizations on hybrid CV-DV circuits. In this work, we introduce a simulation and benchmarking framework for hybrid CV-DV circuits, implemented using Bosonic Qiskit-a tool specifically designed to model CV-DV systems, along with QuTip for functional correctness verification. We construct and characterize representative CV-DV benchmarks, including cat state generation, GKP state generation, CV-DV state transfers, hybrid quantum Fourier transform, variational quantum algorithms, Hamiltonian simulation, and Shor's algorithm. To assess circuit complexity and scalability, we define a feature map organized into two categories: general features (e.g., qubit/qumode count, gate counts) and CV-DV-specific features (e.g., Wigner negativity, energy, truncation cost). These metrics enable evaluation of both classical simulability and hardware resource requirements. Our results, including one benchmark on real hardware, demonstrate that hybrid CV-DV architectures are not only viable but well-suited for a range of computational tasks, from optimization to Hamiltonian simulation. This framework lays the groundwork for systematic evaluation and future development of hybrid quantum systems.

This paper focuses on error thresholds for Pauli channels. We numerically compute lower bounds for the thresholds using the analytic framework of coset weight enumerators pioneered by DiVincenzo, Shor and Smolin in 1998. In particular, we study potential non-additivity of a variety of small stabilizer codes and their concatenations, and report several new concatenated stabilizer codes of small length that show significant non-additivity. We also give a closed form expression of coset weight enumerators of concatenated phase and bit flip repetition codes. Using insights from this formalism, we estimate the threshold for concatenated repetition codes of large lengths. Finally, for several concatenations of small stabilizer codes we optimize for channels which lead to maximal non-additivity at the hashing point of the corresponding channel. We supplement these results with a discussion on the performance of various stabilizer codes from the perspective of the non-additivity and threshold problem. We report both positive and negative results, and highlight some counterintuitive observations, to support subsequent work on lower bounds for error thresholds.

Magic states, by allowing non-Clifford gates through gate teleportation, are important building blocks of fault-tolerant quantum computation. Magic state distillation protocols aim to create clean copies of magic states from many noisier copies. However, the prevailing protocols require substantial qubit overhead. We present a distillation protocol based on permutation-invariant gnu codes, as small as two qubits. The two-qubit protocol achieves a 0.5 error threshold and 1/2 distillation rate, surpassing prior schemes for comparable codes. Our protocol furthermore distils magic states with arbitrary magic by varying the position of the ideal input states on the Bloch sphere. We achieve this by departing from the usual magic state distillation formalism, allowing the use of non-Clifford gates in the distillation protocol, and allowing the form of the output state to differ from the input state. Our protocol is compatible for use in tandem with existing magic state distillation protocols to enhance their performance.

All utility-scale quantum computers will require some form of Quantum Error Correction in which logical qubits are encoded in a larger number of physical qubits. One promising encoding is known as the colour code which has broad applicability across all qubit types and can decisively reduce the overhead of certain logical operations when compared to other two-dimensional topological codes such as the surface code. However, whereas the surface code decoding problem can be solved exactly in polynomial time by finding minimum weight matchings in a graph, prior to this work, it was not known whether exact and efficient colour code decoding was possible. Optimism in this area, stemming from the colour code's significant structure and well understood similarities to the surface code, fanned this uncertainty. In this paper we resolve this, proving that exact decoding of the colour code is NP-hard -- that is, there does not exist a polynomial time algorithm unless P=NP. This highlights a notable contrast to some of the colour code's key competitors, such as the surface code, and motivates continued work in the narrower space of heuristic and approximate algorithms for fast, accurate and scalable colour code decoding.

Atom loss is a major error source in neutral-atom quantum computers, accounting for over 40% of the total physical errors in recent experiments. Unlike Pauli errors, atom loss poses significant challenges for both syndrome extraction and decoding due to its nonlinearity and correlated nature. Current syndrome extraction circuits either require additional physical overhead or do not provide optimal loss tolerance. On the decoding side, existing methods are either computationally inefficient, achieve suboptimal logical error rates, or rely on machine learning without provable guarantees. To address these challenges, we propose the Pauli Envelope framework. This framework constructs a Pauli envelope that bounds the effect of atom loss while remaining low weight and efficiently computable. Guided by this framework, we first design a new atom-replenishing syndrome extraction circuit, the Mid-SWAP syndrome extraction, that reduces error propagation with no additional space-time cost. We then propose an optimal decoder for Mid-SWAP syndrome extraction: the Envelope-MLE decoder formulated as an MILP that achieves optimal effective code distance dloss ~ d for atom-loss errors. Inspired by the exclusivity constraint of the optimal decoder, we also propose an Envelope-Matching decoder to approximately enforce the exclusivity constraint within the MWPM framework. This decoder achieves d_loss ~ 2d/3, surpassing the previous best algorithmic decoder, which achieves dloss ~ d/2 even with an MILP formulation. Circuit-level simulations demonstrate that our approach attains up to 40% higher thresholds and 30% higher effective distances compared with existing algorithmic decoders and syndrome extraction circuits in the loss-dominated regime. On recent experimental data, our Envelope-MLE decoder improves the error suppression factor of a hybrid MLE--machine-learning decoder from 2.14 to 2.24.

To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network. However, this approach introduces a critical challenge, namely the high cost of quantum communication between remote QPUs incurred by quantum state teleportation and quantum gate teleportation. To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs. Static graph partitioning methods are fundamentally ill-equipped for this task as they ignore execution dynamics and underlying network topology, while metaheuristics require substantial computational runtime. In this work, we propose a heuristic based on beam search to solve the circuit partitioning problem. Our time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead. The time and space complexities of the proposed algorithm scale quadratically with the number of qubits and linearly with circuit depth, offering a significant computational speedup over common metaheuristics. We demonstrate that our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies, providing an efficient compilation tool for near-term distributed quantum hardware.

Quantum Singular Value Transformation (QSVT) provides a unified framework for applying polynomial functions to the singular values of a block-encoded matrix. QSVT prepares a state proportional to $\bA^{-1}\bb$ with circuit depth $O(d\cdot\mathrm{polylog}(N))$, where $d$ is the polynomial degree of the $1/x$ approximation and $N$ is the size of $\bA$. Current polynomial approximation methods are over the continuous interval $[a,1]$, giving $d = O(\sqrt{\kap}\log(1/\varepsilon))$, and make no use of any properties of $\bA$. We observe here that QSVT solution accuracy depends only on the polynomial accuracy at the eigenvalues of $\bA$. When all $N$ eigenvalues are known exactly, a pure spectral polynomial $p_{S}$ can interpolate $1/x$ at these eigenvalues and achieve unit fidelity at reduced degree. But its practical applicability is limited. To address this, we propose a spectral correction that exploits prior knowledge of $K$ eigenvalues of $\bA$. Given any base polynomial $p_0$, such as Remez, of degree $d_0$, a $K\times K$ linear system enforces exact interpolation of $1/x$ only at these $K$ eigenvalues without increasing $d_0$. The spectrally corrected polynomial $p_{SC}$ preserves the continuous error profile between eigenvalues and inherits the parity of $p_0$. QSVT experiments on the 1D Poisson equation demonstrate up to a $5\times$ reduction in circuit depth relative to the base polynomial, at unit fidelity and improved compliance error. The correction is agnostic to the choice of base polynomial and robust to eigenvalue perturbations up to $10\%$ relative error. Extension to the 2D Poisson equation suggests that correcting a small fraction of the spectrum may suffice to achieve fidelity above $0.999$.

Fault-tolerant quantum computation (FTQC) requires fast and accurate decoding of quantum errors, which is often formulated as a minimum-weight perfect matching (MWPM) problem. A determinant-based approach has been proposed as a promising method to surpass the conventional polynomial runtime of MWPM decoding via the blossom algorithm, asymptotically achieving polylogarithmic parallel runtime. However, the existing approach requires an impractically large bit length to represent intermediate values during the computation of the matrix determinant; moreover, when implemented on a finite-bit machine, the algorithm cannot detect overflow, and therefore, the mathematical correctness of such algorithms cannot be guaranteed. In this work, we address these issues by presenting a polylog-time MWPM decoder that detects overflow in finite-bit representations by employing an algebraic framework over a truncated polynomial ring. Within this framework, all arithmetic operations are implemented using bitwise XOR and shift operations, enabling efficient and hardware-friendly implementation. Furthermore, with algorithmic optimizations tailored to the structure of the determinant-based approach, we reduce the arithmetic bit length required to represent intermediate values in the determinant computation by more than $99.9\%$, while preserving its polylogarithmic runtime scaling. These results open the possibility of a proof-of-principle demonstration of the polylog-time MPWM decoding in the early FTQC regime.

Topological superconductivity could host quasiparticles that are key candidates for fault-tolerant quantum computation due to their immunity to noise as they obey non-Abelian exchange statistics. For example, in the case of Majorana Zero Modes (MZM), braiding enables two topologically protected quantum gates. While their direct manipulation in solid-state systems remains experimentally challenging, digital emulation of MZM behavior has provided insight as well as a deeper understanding of controlling these topological quantum systems. This emulation is typically accomplished by mapping the topological and trivial phases of a Majorana system to ferromagnetic and paramagnetic Hamiltonians of a spin-glass model. This approach usually relies on adiabatic evolution of superconducting Hamiltonians, which require circuits with very large depths. In this work, we present a resource-efficient method to emulate MZM braiding in a trijunction geometry using a quantum processor. We introduce direct braiding operators which simulate the evolution more efficiently, reducing the quantum gate overhead. We then further generalize this method to emulate braiding operations in extended trijunction architectures based on Kitaev chains.

Quantum crosstalk poses a major challenge to scaling up quantum computations as its strength is typically unknown and its effect accumulates exponentially as system size grows. Here, we show that many-body robust control can be utilized to suppress unwanted couplings during multi-qubit gate operations and state preparation. By combining tensor network simulations with the GRAPE algorithm, and leveraging an efficient random sampling over noise ensembles, our method overcomes the exponential scaling of the Hilbert space. We demonstrate its effectiveness for designing control solutions for high-fidelity implementations of parallel X gates and parallel CNOT on a chain of 50 qubits, and for realizing a 30-qubit GHZ state and the ground state of a 20-qubit Heisenberg model. In the presence of many-body quantum crosstalk due to parasitic interaction between neighboring qubits, robust control results in order-of magnitude improvement in fidelity for large system sizes. These findings pave the way for more reliable operations on near-term quantum processors.

Transversal gates are the simplest form of fault-tolerant gates and are relatively easy to implement in practice. Yet designing codes that support useful transversal operations -- especially non-Clifford or addressable gates -- remains difficult within the stabilizer formalism or CSS constructions alone. We show that these limitations can be overcome using tensor-network frameworks such as the quantum lego formalism, where transversal gates naturally appear as global or localized symmetries. Within the quantum lego formalism, small codes carrying desirable symmetries can be "glued" into larger ones, with operator-flow rules guiding how logical symmetries are preserved. This approach enables the systematic construction of codes with addressable transversal single- and multi-qubit gates targeting specific logical qubits regardless of whether the gate is Clifford or not. As a proof of principle, we build new finite-rate code families that support strongly transversal $T$, $CCZ$, $SH$, and Gottesman's $K_3$ gates, structures that are challenging to realize with conventional methods. We further construct holographic and fractal-like codes that admit addressable transversal inter-, meso-, and intra-block $T$, $CS$, and $C^\ell Z$ gates. As a corollary, we demonstrate that the heterogeneous holographic Steane-Reed-Muller black hole code also supports fully addressable transversal inter- and intra-block $CZ$ gates, significantly lowering the overhead for universal fault-tolerant computation.

We have generalised an all-optical telecorrection protocol for the higher orders of the cat code, and show that with these higher orders we can achieve target performance at substantially reduced iteration counts at the cost of a higher mean photon-number. We also introduce a probabilistic scheme for correcting deformation of the state, which highlights two interesting abilities of telecorrection: to encode new sets of transformations, and to change the basis of the code. We find that for a target channel fidelity of $99.9\%$ over a channel with $1\text{ dB}$ of loss, a third-order cat code requires $70$ times fewer telecorrection iterations than a first-order one, at a cost of a $3.6$-fold increase in mean photon-number.

We show how entanglement-assisted codes can be constructed from arbitrary quantum codes by associating them with quantum codes for erasure channels. If a subset of physical qubits is correctable for an erasure error, then it naturally forms the receiver's share of a bipartite state that can be used for entanglement-assisted communications, both in the noiseless and noisy ebit error models. In the case of degenerate codes, we show that the receiver's share of the bipartite state can sometimes be compressed, at the cost of potentially reduced error-correction ability in the noisy ebit error model. We also give examples of permutation-invariant and XP-stabilizer entanglement-assisted codes, the first outside of the stabilizer and codeword-stabilized frameworks.

The path to fault-tolerant quantum computing hinges on hardware that scales while remaining compatible with quantum error correction (QEC). Silicon spin qubits are a leading hardware candidate because they combine industrial fabrication compatibility with a nanoscale footprint that could accommodate millions of qubits on a chip. However, their suitability for QEC remains uncertain since spatially correlated noise naturally emerges from the resulting close proximity of qubits. These correlations increase the likelihood of simultaneous errors and erode the redundancy that QEC depends on. Here we quantify the spatial extent of noise correlations in a five-qubit silicon array and assess their impact on QEC. We identify two distinct sources of correlated noise: global magnetic field drifts that generate perfectly correlated fluctuations, and charge noise from two-level fluctuators that produces short-range correlations decaying within neighboring qubits. While magnetic drifts represent a critical correlated noise source that can compromise QEC, they can be mitigated. In contrast, the measured charge noise correlations are moderate, electrically tunable, and compatible with fault-tolerant operation with minimal qubit overhead. Our results establish quantitative benchmarks for correlated noise and clarify how such correlations impact the viability of quantum error correction in scalable qubit arrays.

Unitary Synthesis, the decomposition of a unitary matrix into a sequence of quantum gates, is a fundamental challenge in quantum compilation. Prevailing reinforcement learning(RL) approaches are often hampered by sparse reward signals, which necessitate complex reward shaping or long training times, and typically converge to a single policy, lacking solution diversity. In this work, we propose QFlowNet, a novel framework that learns efficiently from sparse signals by pairing a Generative Flow Network (GFlowNet) with Transformers. Our approach addresses two key challenges. First, the GFlowNet framework is fundamentally designed to learn a diverse policy that samples solutions proportional to their reward, overcoming the single-solution limitation of RL while offering faster inference than other generative models like diffusion. Second, the Transformers act as a powerful encoder, capturing the non-local structure of unitary matrices and compressing a high-dimensional state into a dense latent representation for the policy network. Our agent achieves an overall success rate of 99.7% on a 3-qubit benchmark(lengths 1-12) and discovers a diverse set of compact circuits, establishing QFlowNet as an efficient and diverse paradigm for unitary synthesis.

The stringent demands of photonic quantum computing protocols motivate photonic integrated circuit (PIC) platforms with passive optical properties such as extremely low losses and correspondingly large circuit depths, as well as active optical properties such as high reconfiguration rates, low power dissipation, and minimal crosstalk. At the same time, many quantum photonic resource state generators, such as single-photon sources and quantum memories, require operation in the visible wavelength range. These requirements make the passive optical properties of CMOS-fabricated, ultralow-loss, low-confinement silicon nitride waveguides especially attractive. However, the conventional active properties of these systems based on thermo-optic modulation are plagued by high levels of crosstalk, slow modulation rates, and high power dissipation. Although there have been recent demonstrations of CMOS-fabricated, visible wavelength, piezo-optomechanical PICs that solve the above challenges associated with implementing active functionality, these have made use of high-confinement waveguides with currently demonstrated losses of order $0.3$-$1~\mathrm{dB/cm}$, precluding circuit depths required for scalable quantum algorithms. Here, we demonstrate that combining piezo-optomechanical actuation with a low-confinement, ultra-low loss silicon nitride platform addresses the scalability challenge while enabling high-performance active functionality at visible wavelengths. This platform achieves a propagation loss $0.026~\mathrm{dB/cm}$ at $780~\mathrm{nm}$, modulation bandwidths in the MHz range, and a phase shifter voltage-length product ($V_πL$) of approximately $2.8~\mathrm{\mathrm{V}\cdot\mathrm{m}}$ and negligible hysteresis. We further demonstrate reconfigurable Mach-Zehnder interferometers based on spiral phase shifters with 0.63 dB loss per phase shifter.

Continuous measurement-based holonomic quantum computation provides a route to universal logical computation in quantum error correcting codes. We introduce a fault-tolerant framework for implementing measurement-based holonomic gates that leverages continuous measurements with real-time feedback. We show that non-Markovian decoherence is intrinsically suppressed through the quantum Zeno effect, while Markovian errors are identified by the decoding of measurement records to reveal the rotated syndrome subspace populated during the evolution. This information enables steering holonomic paths mid-flight to ensure that the final evolution realizes the target logical gate. We further demonstrate that non-adiabatic effects give rise to measurement-induced errors, and we show that these can also be corrected by an analogous protocol. This approach relaxes the stringent adiabaticity requirement and enables faster implementation of holonomic gates.

Generalized code surgery is a versatile and low-overhead technique for performing fault-tolerant computation on quantum low-density parity-check (qLDPC) codes. In many settings, surgery exhibits practical space overheads, while its time overhead remains a bottleneck at $O(d)$ syndrome rounds per operation. In this work, we construct surgery gadgets that perform parallel logical measurements on 2D hypergraph product codes in constant time overhead ($O(1)$) and near-constant space overhead ($\tilde{O}(1)$). The reduced time overhead is a result of amortization, as we show, following the formulation by Cowtan et al. (arXiv:2510.14895), that performing $d$ surgery operations in $O(d)$ time is fault tolerant. Our gadgets combine the strengths of different approaches to fault-tolerant logical operations: they partially retain the flexibility of surgery while achieving overheads comparable to transversal gates. Consequently, they are well-suited for near-term experimental realization and demonstrate new possibilities in the design of gadgets for fast logical computation.

Time-continuous quantum error correction, necessary to protect quantum information under time-dependent Hamiltonians, relies on weak continuous syndrome measurements. Implementing these measurements requires a continuous coupling among at least two qubits and a meter, a demanding requirement. We show that, under continuous operation, common parity-measurement protocols in the circuit quantum electrodynamics platform corrupt the logical information. The failure arises from approximating the three-body interaction by a sum of two-body couplings to the meter, which prevents simultaneous suppression of measurement backaction on the logical and error subspaces. We argue that the same mechanism applies more generally beyond the circuit quantum electrodynamics setting. Taken together, our results impose a practical limitation on continuous stabilizer quantum error correction and point to the viable alternatives -- architectures that realize native three-body interactions, or erasure-based encodings in which the error subspace need not be protected.

Hook errors are a major challenge in implementing logical operations with the surface code, because they can reduce the fault distance below the code distance. This motivates syndrome-extraction circuits that suppress hook-error effects for the stabilizer layouts that appear during logical operations. However, the existing methods either increase circuit depth or require simultaneous execution of measurements and CNOT gates, both of which introduce additional overheads and degrade the threshold. We propose the ZX interleaving syndrome extraction, which preserves the full fault distance $d$ for any surface-code layout with regular stabilizer tiles at minimum depth, i.e., four layers of CNOT gates, without requiring additional circuit depth or simultaneous execution of measurements and CNOT gates. The key idea is to interleave the Z and X stabilizer tiles so that hook-error edges in the decoding graph are shortened and effectively eliminated. Numerical simulations under uniform depolarizing noise for memory and lattice-surgery experiments confirm that the proposed method achieves a full fault distance of $d$, whereas the best existing minimum-depth approach achieves $d-1$. Since the full fault distance is achievable for any regular tiling layout of the surface code, the proposed method may serve as an indispensable technique for practical fault-tolerant quantum computation.

The realization of fault-tolerant quantum computation hinges on the ability to execute deep quantum circuits while maintaining gate fidelities consistently above error-correction thresholds. Although neutral-atom arrays have recently demonstrated high-fidelity two-qubit gates and early-stage logical quantum processors, sustaining such high performance across deep, repetitive circuits remains a formidable challenge due to cumulative motional heating and atom loss. Here we demonstrate a sustainable neutral-atom framework that overcomes these limitations by integrating a suite of hardware-efficient mid-circuit operations. We report a two-qubit controlled logic gate with a raw fidelity of 99.60(1)%, which is further increased to a fidelity of 99.81(1)% via non-destructive erasure detection. Crucially, by implementing in-circuit Raman sideband cooling and qubit re-initialization, we demonstrate that gate fidelities can be maintained at the ~99.8% level across multiple operational rounds without observable degradation. By actively managing the internal and motional entropy of the system mid-stream, our in-situ refreshable architecture provides a critical pathway for executing the repeated syndrome-extraction cycles required for large-scale, continuous quantum error correction.

The qubit mapping problem is a challenge in quantum computing that is related to mapping logical qubits to the physical ones on the quantum computer. Due to the diversity of quantum computer topologies and circuits, numerous approaches solving this problem exist. Finding the best solution for specific combination of topology and circuit remains difficult and no unified framework currently exists for systematically evaluating and comparing qubit mapping algorithms across different cases. We present QuMeld, an open-source framework that is designed for solving this issue. The framework currently supports six qubit mapping algorithms, sixteen quantum computer topologies and multiple evaluation metrics. The modular design of the framework allows integration of new mapping algorithms, quantum circuits, hardware topologies, and evaluation metrics, ensuring extensibility and adaptability to future developments.

We find the dispersion relations of two elusive families of core-bound excitations of the Gross-Pitaevskii (GP) vortex, varicose (axisymmetric) and fluting (quadrupole) waves. For wavelengths of order the healing length, these two families -- and the well-known Kelvin wave -- possess an infinite sequence of core-bound, vortex-specific branches whose energies lie below the Bogoliubov dispersion relation. In the short-wavelength limit, these excitations can be interpreted as particles radially bound to the vortex, which acts as a waveguide. In the long-wavelength limit, the fluting waves unbind from the core, the varicose waves reduce to phonons propagating along the vortex, and the fundamental Kelvin wave is the only core-bound vortex-specific excitation. Finally, we propose a realistic spectroscopic protocol for creating and detecting the varicose wave, which we test by direct numerical simulations of the GP equation.

Continuous-variable quantum systems are central to quantum technologies, with Gaussian states playing a key role due to their broad applicability and simple description via first and second moments. Distinguishing Gaussian states requires computing their trace distance, but no analytical formula exists for general states, and numerical evaluation is difficult due to the exponential cost of representing infinite-dimensional operators. We introduce an efficient numerical method to compute the trace distance between a pure and a mixed Gaussian state, based on a generalized Lanczos algorithm that avoids explicit matrix representations and uses only moment information. The technique extends to non-Gaussian states expressible as linear combinations of Gaussian states. We also show how it can yield lower bounds on the trace distance between mixed Gaussian states, offering a practical tool for state certification and learning in continuous-variable quantum systems.

Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust simulation protocols, and developing tailored error-correction methods. Under Markovian noise/dissipation, a natural characterization approach is to identify the full Lindbladian generator that gives rise to both coherent (Hamiltonian) and dissipative dynamics. Prior protocols for learning Lindbladians from dynamical data assumed pre-specified interaction structure, which can be restrictive when the relevant noise channels or control imperfections are not known in advance. In this paper, we present the first sample-efficient protocol for learning sparse Lindbladians without assuming any a priori structure or locality. Our protocol is ancilla-free, uses only product-state preparations and Pauli-basis measurements, and achieves near-optimal time resolution, making it compatible with near-term experimental capabilities. The final sample complexity depends on linear-system conditioning, which we find empirically to be moderate for a broad class of physically motivated models. Together, this provides a systematic route to scalable characterization of open-system quantum dynamics, especially in settings where the error mechanisms of interest are unknown.

We investigate the quantum algorithm of Babbush et al. (arXiv:2303.13012v3) for simulating coupled harmonic oscillators, which promises exponential speedups over classical methods. Focusing on linearly connected oscillator chains, we bridge the gap between theory and implementation by developing and comparing three concrete realizations of the algorithm. First, we implement a sparse initial state preparation combined with product-formula (Suzuki-Trotter) Hamiltonian simulation. Second, we implement a fully quantum, oracle-based framework in which classical data are accessed via oracles, the Hamiltonian is block-encoded, and time evolution is performed using QSVT-based Hamiltonian simulation. Third, we propose an efficient alternative that combines the sparse state-preparation routine of the first approach with the oracle and block-encoding-based simulation pipeline of the second. We provide these implementations on Classiq, a high-level quantum design platform and provide appropriate resource benchmarks. Our simulation results show that the complex initial state preparation proposed by Babbush et al. can be circumvented at least in the linear-chain case. Finally, we illustrate two physical applications-extracting normal modes and simulating coarse-grained energy propagation-demonstrating how the algorithm connects to measurable observables. Our results clarify the resource requirements of the algorithm and provide concrete pathways toward practical quantum advantage.

Quantum-gas microscopes provide direct access to the phases of the Hubbard model, bringing microscopic insight into the complex competition between interactions, SU(2) magnetism, and doping. Alkaline-earth(-like) fermions extend this spin-1/2 paradigm by realizing higher symmetries and giving access to SU(N) Hubbard models, with rich phase diagrams to be unveiled. Despite its fundamental interest, a microscopic exploration of SU(N) quantum systems has remained elusive. Here we report the realization of a quantum-gas microscope for fermionic $^{87}$Sr. Our imaging scheme, based on cooling and fluorescence on the narrow intercombination line at 689 nm, enables spin-resolved single-atom detection. By implementing a spin-selective optical pumping protocol, we determine the occupation of each of the 10 spin states in a single experimental realization, a crucial capability for probing site-resolved magnetic correlations. We benchmark our method by observing single-particle Larmor precession across the full spin-9/2 ground-state manifold. These results establish $^{87}$Sr quantum-gas microscopy as a powerful approach to study exotic magnetism in the SU(N) Fermi-Hubbard model, and provide a new detection tool for studies in quantum simulation, computation, and metrology.

Quantum memory is a scarce and costly resource, yet little is known about which learning tasks remain feasible under severe memory constraints. We study the problem of computing global properties of quantum sequences when quantum systems must be measured individually, without storing or jointly processing them. In our setting, a bit string $x \in \{0,1\}^n$ is encoded into an $n$-qubit product state $|ψ_{x_1}\rangle \otimes \cdots \otimes |ψ_{x_n}\rangle$, and the goal is to infer $f(x) \in \{0,1\}$ from measurements of this quantum encoding. We consider a simple local strategy, which we call the greedy strategy, that applies the same optimal single-system measurement independently to each subsystem and then infers $f(x)$ from the outcomes. Our main result gives a complete characterization of when the greedy strategy is optimal: it achieves the same maximum success probability as an unrestricted global measurement if and only if the target Boolean function is affine (in all but finitely many cases). We establish a universal performance guarantee for general Boolean functions, showing that the success probability of the greedy strategy is always at least the square of the optimal global success probability, in direct analogy with the Barnum-Knill bound for the pretty good measurement. These results demonstrate that even under extreme memory constraints, simple local measurement strategies can remain provably competitive for learning global properties of quantum sequences.

In this work, we numerically study the effect of weak measurement on deconfined quantum critical point(DQCP). Particularly, we consider the ground state of an one-dimensional spin $1/2$ system with long range exchange interactions($K$), which shows analogues phase transition to DQCP in the thermodynamic limit. This system is in the ferromagnetic phase below the critical exchange interaction $K_c$ and in the valance bond solid phase above $K_c$. The weak measurement is carried out by coupling a secondary ancilla system to the critical system via unitary interactions and later measuring the ancilla spins projectively. We numerically calculate entanglement entropy,correlation length, and order parameters of leading post-measurement states using uniform matrix product state representation of the quantum many-body state in the thermodynamic limit. We report asymmetric restructuring of entanglement of the post measurement states across the phase boundary under weak measurements. Especially, the trajectory $\left(\downarrow \downarrow\right)$ describing a uniform measurement outcome given the all ancilla spins initiated in the same $\left(\downarrow \right)$ state, shows anomalous entanglement when increasing the strength of weak measurement. The bipartite entanglement entropy strongly increases when $K<K_c$ whereas it weakly decreases when $K>K_c$. We argue with numerical evidences that observed asymmetry in entanglement would lead to a weak first order phase boundary in the thermodynamic limit. We also discuss important aspects in experimental observation of measurement induced effects linked to the strength of weak measurement and probability of post-measurement states.

We introduce Extreme Quantum Cognition Machines, a class of quantum learning architectures for deliberative decision making that is tolerant to noisy and contradictory training data. Inspired by the quantum cognition paradigm, Extreme Quantum Cognition Machines are closely related to quantum extreme learning and quantum reservoir computing, where fixed quantum dynamics generates a nonlinear feature map and learning is confined to a linear readout. A dynamical attention mechanism, implemented through an input-dependent interaction term in the Hamiltonian, modulates the quantum evolution and biases the resulting feature embedding toward task-relevant correlations. The approach is validated on linguistic classification tasks, which serve as paradigmatic examples of deliberative inference. Hardware-compatible quantum implementations of the proposed framework are discussed, together with potential applications in symbolic inference, sequence analysis, anomaly detection, and automatic diagnosis, with direct relevance to domains such as biology, forensics, and cybersecurity.

Quantum tracking control encodes the desired dynamics into a tailored driving field; here, we let the system find its own way there. We propose a real-time feedback control framework in which a proportional controller continuously corrects a simple transform-limited field based on the instantaneous mismatch between two systems' responses - producing the required control on the fly, without prior waveform design. The framework is demonstrated on two distinct examples: a single-active-electron atom, where hydrogen is driven to mimic argon's strong-field optical emission, and a Fermi-Hubbard chain, where a weakly interacting lattice reproduces the transport dynamics of a Mott-insulating reference. By shifting the control paradigm from predesigned inputs to adaptive response tracking, this approach establishes closed-loop feedback as a broadly applicable route to programmable quantum dynamics.

Spin-lattice relaxation theory has been developed over almost a century, but some cardinal assumptions on the nature of the interactions involved have never been fully verified. This includes the weak coupling approximation, which makes it possible to describe spin dynamics perturbatively and leads to the canonical description of spin relaxation in terms of one- and two-phonon processes. Here, we extend the first-principles theory of spin relaxation to three-phonon processes and apply it to the vdW crystal of a spin-1/2 Chromium nitride complex. Results show that three-phonon contributions to spin relaxation only become relevant at temperatures inaccessible to experiments for this molecule, thus providing unprecedented evidence for the validity of the weak spin-phonon coupling assumption in spin relaxation theory. At the same time, we numerically show that a relatively small increase in spin-phonon coupling would lead to a crossover between three- and two-phonon processes' efficiency at room temperature, illustrating the possibility for three-phonon effects in molecular materials as well as paving the way to a systematic exploration of strong coupling in spin systems.

Simulating the dynamics of molecular excitons in complex nanophotonic environments requires integrating rigorous electromagnetic simulations with accurate treatments of open quantum system dynamics. In this work, we develop MQED-QD (Macroscopic Quantum Electrodynamics for Quantum Dynamics), a robust computational package for simulating exciton dynamics in arbitrary dielectric and plasmonic environments. Based on the MQED framework, the package offers a unified workflow for constructing the dyadic Green's functions from classical electromagnetic solvers, parametrizing quantum master equations, and propagating the time evolution to determine the molecular subsystem's dynamical properties. To demonstrate the package's capabilities, we simulate exciton transport within a one-dimensional molecular chain near a silver nanostructure, including benchmarking against planar surfaces and exploring the influence of silver nanorods. Our results reveal that surface plasmon polaritons on nanorods dramatically enhance long-range dipole-dipole interactions, accelerating exciton delocalization and yielding higher participation ratios compared to planar geometries. By elucidating accurate molecular exciton dynamics in conjunction with nanophotonics and plasmonics, MQED-QD provides a powerful, open-source package that facilitates the rational design of nanoscale architectures.

Nonreciprocal phenomena are currently a major focus of research within the fields of classical and quantum technology. In this work, we theoretically investigate the interplay among multiple magnomechanically induced transparency (MMIT) windows, Fano resonances, slow/fast light, and nonreciprocal absorption and group delay in a hybrid cavity magnomechanical system. This system is composed of two yttrium iron garnet (YIG) spheres and a membrane positioned at the center of the cavity. By analyzing the absorption spectrum of a weak probe field in the presence of a strong control field, we demonstrate the emergence of five transparency windows resulting from combined photon-phonon, photon-magnon, and phonon-magnon interactions. The photon-phonon coupling associated with the membrane plays a crucial role in enhancing and tailoring these transparency features. We further examine the impact of the Barnett effect on the absorption and dispersion characteristics, showing that it enables the controllable manipulation of transparency windows and the generation of tunable Fano resonance profiles. The influence of cavity decay and magnon dissipation rates on the spectral response is also analyzed. In addition, we demonstrate that the group delay of the transmitted probe field can be effectively tuned via the photon-phonon coupling strength and the Barnett effect, allowing for a controllable transition between slow and fast light regimes. Finally, nonreciprocal absorption and group delay are achieved through appropriate adjustment of the coupling parameters. These findings highlight the potential of the proposed hybrid system for applications in optical signal processing and quantum information technologies.

We present a quantum-classical hybrid implementation of the Liouvillian recursion method to compute many-body Green's functions using a quantum computer. From an approximate ground state preparation circuit, this algorithm produces the local ($r=r'$) and inter-site ($r\neq r'$) Green's functions $G_{rr'}(ω)$ by measuring observables generated recursively. We demonstrate the approach on a superconducting quantum processor for the open-boundary four-site Hubbard model. We then use the computed Green's functions as input to the Galitskii-Migdal formula to produce better ground state energy estimation than the expectation value of the Hamiltonian for the approximate circuit. Empirical results indicate exponential convergence in the number of iterations, yielding a computational complexity polynomial in the Green's-function accuracy, as measured with the Wasserstein distance. Our results also indicate significant robustness to noise and to inaccuracies of the ground state preparation, providing evidence that Liouvillian recursion is well adapted to the constraints of near-term quantum computing.

High harmonic generation (HHG) in solids results from an interplay between intraband acceleration and electron-hole recombination driven by a high-intensity laser pulse. Here, we theoretically reveal that the driving pulse duration can play a major role in extending HHG to higher photon energies by promoting higher conduction band excitations. The effect is present in a conventional semiconductor as Si, restricted in a large-gap insulator as MgO, and most prominent in RhSi, a prototypical chiral Weyl semimetal presenting numerous band crossings. Further, we elucidate the HHG selection rules in RhSi required for the synthesis of attosecond locally chiral light. The chiral crystal structure enables the generation of a local 3D electric field exhibiting an asymmetric instantaneous torsion on attosecond timescales. A pronounced circular dichroism emerges when the driving helicity is either aligned with or opposite to the crystal handedness. Our findings motivate future experiments in chiral Weyl semimetals to track high-energy band crossings and in-situ locally chiral light, paving the way for chiral compact light sources and light-wave driven topological electronics.

We study the mode dynamics of a generic quadratic fermionic Hamiltonian under a sudden quench protocol in momentum space. Modes with zero energy at any given time, $t$, are referred to as dynamical critical modes. Among all zero-energy modes, spin-flip symmetry is restored in the eigenvector corresponding to selected zero-energy modes. This symmetry restoration is used to define the dynamical quantum phase transition (DQPT). This shows that the occurrence of these dynamical critical modes is necessary but not sufficient for a DQPT. We show that the conditions on the quench protocol and time for such dynamical symmetry restoration are the same as the divergence of the rate function and integer jump in the dynamical topological order parameter, which have been the traditional identifiers of a DQPT. This perspective also naturally explains when one or both of DQPT and ground-state quantum phase transitions will occur.

Optimization is ubiquitous in quantum information science and technology, however, the corresponding optimization landscape can encounter false traps, i.e., local but not global optima, likely to prevent used optimizers from finding optimal solutions. Such traps are believed to arise from parameter insufficiency and are expected to disappear when tunable parameters are sufficiently abundant. In this work, we investigate optimization landscapes of quantum optimization problems, and especially obtain that the parameter sufficiency is not enough to ensure the absence of false traps. First, we present a complete framework for analyzing critical features of optimization landscapes, by deriving necessary and sufficient conditions to identify all critical points and to classify them as local maxima, minima, or saddles, under some assumptions. Then, we show that false traps can still emerge on landscapes even with sufficient parameters, implying their appearance cannot be solely attributed to parameter insufficiency. Moreover, a close connection between landscape topology and quantum distinguishability is revealed that the emergence of false traps is linked to the loss of distinguishability among states or operators in the objective function. Finally, implications of our results are noted. Our work not only provides a deeper understanding of the intrinsic complexity of quantum-classical optimization, but also provides practical guidance for solving quantum-classical optimization problems, thus significantly aiding the progress in witnessing quantum advantages of the underlying quantum information processing tasks.

The Quantum Approximate Optimization Algorithm (QAOA) is expected to offer advantages over classical approaches when solving combinatorial optimization problems in the Noisy Intermediate-Scale Quantum (NISQ) era. In its standard formulation, however, QAOA is not suited for constrained problems. One way to incorporate certain types of constraints is to restrict the mixing operator to the feasible subspace; however, this substantially increases circuit size, thereby reducing noise robustness. In this work, we refine an existing hypercube mixer method for enforcing hard constraints in QAOA. We present a modification that generates circuits with fewer gates for a broad class of constrained problems defined by linear functions. Furthermore, we calculate an analytical upper bound on the number of binary variables for which this reduction might not apply. Additionally, we present numerical experimental results demonstrating that the proposed approach improves robustness to noise. In summary, the method proposed in this paper allows for more accurate QAOA performance in noisy settings, bringing us closer to practical, real-world NISQ-era applications.

Unidimensional (1D) Gaussian-modulated continuous-variable quantum key distribution protocols have been proposed as a way to simplify implementation and reduce costs through single-quadrature modulation, requiring only one modulator while maintaining compatibility with standard optical infrastructure. Here, we determine security bounds for 1D discrete-modulated protocol under the Gaussian extremality assumption by extending the method of Ghorai et al. [Phys. Rev. X 9, 021059 (2019)]. We establish the appropriate symmetry arguments to extend the method to the 1D discrete-modulated case, define the physicality zone in which the protocol is allowed to operate, and prove security against collective attacks in the asymptotic regime via semidefinite programming. Our analysis for uniformly distributed coherent states reveals a fundamental limitation: the Gaussian extremality assumption systematically overestimates Eve's information with increasing constellation size, yielding bounds so conservative that secure key extraction becomes impossible for constellations larger than four states, even under ideal conditions. This overestimation worsens with excess noise and restricts viable modulation amplitudes to impractically small values. Unlike two-dimensional (2D) protocols, where Gaussian extremality improves with constellation size, 1D protocols lack the growing phase-space isotropy required for the approximation to remain tight as the constellation grows. Our results expose these limitations and highlight the necessity of alternative methods or optimized non-uniform constellation designs for this class of protocols.

We train machine learning algorithms to infer the entanglement structure of disordered long-range interacting quantum spin chains by learning from the strong disorder renormalisation group (SDRG) method. The system consists of $S=1/2$-quantum spins coupled by antiferromagnetic power-law interactions with decay exponent $α$ at random positions on a one-dimensional chain. Using SDRG as a physics-informed teacher, we compare a Random Forest classifier as a classical baseline with a graph neural network (GNN) that operates directly on the interaction graph and learns a bond-ranking rule mirroring the SDRG decimation policy. The GNN achieves a disorder-averaged pairing accuracy close to one and reproduces the entanglement entropy $S(\ell)$ in excellent quantitative agreement with SDRG across all subsystem sizes and interaction exponents. RG flow heat maps confirm that the GNN learns the sequential decimation hierarchy rather than merely fitting final-state observables. Finite-temperature entanglement properties are incorporated via the SDRGX framework through a two-stage strategy, using the zero-temperature GNN to generate the RG flow and sampling thermal occupations from the canonical ensemble, yielding results in agreement with both numerical SDRGX and analytical predictions without retraining.

Dynamic quantum circuits combine mid-circuit measurement with classical feed-forward, enabling circuit constructions with reduced entangling-gate depth. Here, we investigate their use in Quantum Imaginary Time Evolution (QITE), where circuit depth and parameter growth limit practical implementations of ground-state preparation. For dense classical optimization Hamiltonians, we introduce a reduced-parameter QITE ansatz that restricts entanglement generation via a small set of control qubits, enabling each QITE layer to be implemented with constant two-qubit gate depth using fan-out-based dynamic circuits. In noiseless simulations of exact cover and set partitioning instances, the reduced ansatz yields a higher success probability than standard QITE approaches. We implement unitary, dynamic fan-out, and semi-classical adaptive variants on IBM superconducting hardware. The semi-classical variant performs favorably to the unitary implementation, while the fully dynamic construction exposes the trade-offs between entangling-depth reduction and measurement and feed-forward overhead associated to dynamic circuit implementations. Using a fidelity threshold of 0.5 relative to the noiseless QITE ansatz, we show that dynamic fan-out based QITE would outperform unitary implementations on current devices when the measurement and two-qubit gate errors are reduced by 65% and the feedback latency is halved.

Classical tensor network and hybrid quantum-classical algorithms are promising candidates for the investigation of real-time properties of lattice gauge theories. We develop here a novel framework which enforces gauge symmetry via a quantum-link virtual rishon representation applied at intermediate steps. Crucially, the gauge and matter degrees of freedom are dynamical variables encoded in terms of qubits, enabling analysis of gauge theories in $d+1$ spacetime dimensions. We benchmark this framework in a U(1) gauge theory with and without matter fields. For $d = 1$, the multi-flavor Schwinger model with $1\leq N_f\leq3$ flavors is analyzed for arbitrary boundary conditions and nonzero topological angle, capturing signatures of the underlying Wess-Zumino-Witten conformal field theory. For $d = 2$, we extract the confining string tension in close agreement with continuum expectations. These results establish the virtual rishon framework as a scalable and robust approach for the simulation of lattice gauge theories using both classical tensor networks as well as near-term quantum hardware.

Understanding the fundamental limits of state convertibility is crucial for establishing the boundaries of quantum information processing and thermodynamic efficiency. While auxiliary systems, catalysts, can facilitate otherwise impossible transformations, standard catalysis rigidly requires the auxiliary system to return to its exact initial state. In this work, we investigate the power of flexible catalysis, where the catalyst evolves through a cycle of states, restoring its initial configuration only after a finite number of steps. Focusing on the regime of fixed, finite dimensions, we analyze the capabilities of flexible catalysis within the resource theories of entanglement and quantum thermodynamics. In the context of entanglement, we derive conditions limiting flexible catalysts and demonstrate that they offer a strict advantage in the success probability of stochastic local operations and classical communication. Conversely, in quantum thermodynamics, we prove that flexible catalysis strictly outperforms standard catalysis even in deterministic settings. We provide an example identifying state transformations that are impossible with any standard catalyst of fixed dimension and Hamiltonian but become achievable via a flexible cycle.

With an increasingly diverse portfolio of quantum backends, the adoption of standardized interfaces has become a key prerequisite for scalable access and interoperability within quantum software stacks. The Quantum Device Management Interface (QDMI) addresses this challenge and is emerging as one of the de facto standards for hardware abstraction, enabling the unified management not only of individual Quantum Processing Units (QPUs) but also of complete full-stack cloud services. This paper presents a case study demonstrating the integration of QDMI with Amazon Braket, a quantum computing cloud service that provides a single access point to a wide range of hardware technologies. By treating the cloud service itself as a unified device, the proposed implementation enables management of the complete task lifecycle - ranging from authentication and circuit submission to result retrieval - across Braket's heterogeneous set of simulators and hardware backends. We detail the engineering insights gained from this integration and present a hands-on example workflow, ultimately paving the way for integrated access to cloud-hosted quantum resources from QDMI-enabled software stacks.

Classical shadow tomography has emerged as a powerful framework for predicting properties of quantum many-body systems with favorable sample complexity. Standard theoretical guarantees, however, rely on the assumption that experimental rounds are independent and identically distributed (i.i.d.). This idealization is often violated in practice, where parameter drift, environmental noise, and active feedback generate history-dependent sequences of states or channels. To address this, we introduce a robust classical shadow protocol based on a truncated mean estimator. We prove that its sample complexity for predicting properties of the time-averaged state or channel matches the standard i.i.d. scaling governed by the shadow norm, even when experimental rounds depend arbitrarily on the past. Our results establish the robustness of the shadow formalism beyond the i.i.d. regime.

We numerically investigate the nonlinear dynamics of a two-dimensional exciton-polariton quantum fluid coherently driven by two counter-propagating laser beams. Using an exciton-photon coupled driven-dissipative Gross-Pitaevskii framework, we identify four distinct regimes-linear, solitonic, turbulent, and superfluid-emerging from the interplay between pump strength, laser detuning, and injected momentum, which together control the balance between kinetic and interaction energies in the quantum fluid. The different regimes are characterized through real-space and momentum-space observables, as well as through the temporal first-order coherence function. We show that turbulence occupies a well-defined and extended region of parameter space, marked by spontaneous vortex nucleation, and a pronounced reduction of temporal coherence, providing a clear signature of nonstationary dynamics. By constructing quantitative phase diagrams, we delineate the transitions between the various regimes and identify multiple pathways connecting solitonic, turbulent, and superfluid behaviors. Finally, we demonstrate that the turbulent regime persists over experimentally realistic parameter ranges compatible with state-of-the-art GaAs-based micro-cavity platforms, establishing counter-propagating polariton flows as a robust and versatile setting for the study of driven-dissipative quantum turbulence in two dimensions.

Screened scalar fields such as the symmetron provide a viable description of dark energy yet their laboratory detection remains challenging. We propose an optomechanical scheme to constrain symmetron interactions using two optically levitated nanospheres inside a cavity. The symmetron-mediated interaction induces an effective coupling which leads to a measurable splitting in the optomechanical resonance spectrum. We forecast constraints in the regime $μ\sim 10^{-2}$eV-$10^{-4}$ eV, which shows that this approach can improve existing laboratory bounds by up to several orders of magnitude, demonstrating the sensitivity of optomechanical spectroscopy to screened fifth forces.

For classical field theories with probabilistic initial conditions the classical field observables are an idealization. Their arbitrarily precise values poorly reflect the characteristic uncertainty in the presence of substantial fluctuations. We propose to describe this system by observables based on fluctuating fields. In terms of these "statistical observables" the probabilistic classical field theory becomes a quantum field theory. Non-commuting operators are associated to observables. The quantum rules follow from the laws for classical probabilities. We construct the functional integral for the quantum field theory, and discuss in detail the classical relativistic Klein-Gordon equation with interactions.

We experimentally validate a unified input--output model that incorporates internal and external coupling phases in a room-temperature cavity magnonic system. By explicitly accounting for phase effects, the model provides full control of interference-induced antiresonances and enables a clear interpretation of the transition from level repulsion to level attraction. Nonreciprocal transmission -- originating from internal phases -- is accurately reproduced under specific coupling conditions. Quantitative agreement between experiments and simulations is obtained across all coupling regimes, demonstrating a practical route toward phase-controlled cavity--magnon devices.

The process tensor framework to open quantum systems provides the most general description of multi-time correlations in non-Markovian quantum dynamics. A compressed representation of a process tensor in terms of matrix product operators (MPO) can be used for numerically exact calculations of multi-time correlation functions in systems strongly coupled to a non-Markovian reservoir. We show here that the numerical scaling for computing multi-dimensional spectra can be significantly improved using a time-translation invariant MPO representation of the process tensor obtained from the uniform time-evolving matrix product operator (uniTEMPO) method. In particular, this approach provides a spectral representation of the non-Markovian dynamics that gives direct access to correlation functions in Fourier-space, avoiding explicit real-time evolution. We calculate linear and 2D electronic spectra for an example system and discuss the performance and numerical scaling of our simulations.

Determining the physically accessible unitary dynamics of a quantum system under finite Hamiltonian resources is a central problem in quantum control and Hamiltonian engineering. Dynamical Lie algebras (DLAs) provide the fundamental link between available control Hamiltonians and the resulting quantum dynamics. While the structural classification of DLAs is well-established, how to systematically engineer and reshape these algebraic structures under realistic physical constraints remains largely unexplored. In this work, building upon recent results on direct sums of identical DLAs, we develop a unified framework for engineering Hamiltonian-driven quantum dynamics based on DLAs: (i) constructing qubit-efficient direct-sum Hamiltonian structures via spectral decomposition of Hermitian operators, enabling parallel simulation of multiple quantum subsystems; (ii) identifying Hamiltonian modifications that preserve full controllability, including the $\mathfrak{su}(2^N)$ algebra, even when additional physically motivated control terms are introduced; and (iii) engineering restricted Hamiltonian sets that confine quantum dynamics to target subalgebras through irreducible Lie-algebra decompositions, providing a principled approach to symmetry-based dynamical reduction. By bridging these Lie-algebraic insights with practical control objectives, our framework provides a systematic pathway for engineering expressive and resource-efficient unitary evolutions, thus unlocking greater structural flexibility of Hamiltonian-driven quantum systems.

As quantum computing hardware rapidly advances, objectively evaluating the capabilities and error rates of new processors remains a critical challenge for the field. A clear and realistic understanding of current quantum performance is essential to guide research priorities and drive meaningful progress. In this work, we apply and extend a protocol-based benchmarking methodology (presented in arXiv:2505.12441) that utilizes well-defined quantumness thresholds. By evaluating performance at protocol level rather then the gate level, this approach provides a transparent and intuitive assessment of whether specific quantum processors, or isolated sub-chips within them, can demonstrate a practical quantum advantage. To illustrate the utility of this method, we compare two generations of IBM quantum computers: the older Eagle architecture and the newer Heron architecture. Our findings reveal the genuine operational strengths and limitations of these devices, demonstrating substantial performance improvements in the newer Heron generation.

Hermiticity is usually treated as a foundational axiom of quantum mechanics, guaranteeing real spectra and unitary time evolution. In this work we argue that Hermiticity is more naturally understood as a symmetry law arising from the global conservation of an inner product current. We show that in spacetimes admitting complete Cauchy surfaces without boundary flux this conservation reduces to the familiar Hermiticity condition of the canonical inner product. However, in the presence of causal horizons, most strikingly in black hole geometries, this conservation law becomes obstructed for restricted observers. Tracing over inaccessible degrees of freedom then inevitably yields completely positive trace preserving dynamics with an effective non-Hermitian generator. Using quantum thermodynamics and the monotonicity of relative entropy, we demonstrate that the generalized second law may be reinterpreted as an entropy balance that compensates precisely for the flux of inner product charge through the horizon. The structure of Einstein equations, through the Bianchi identity and the Raychaudhuri focusing equation, provides the geometric mechanism underlying this balance. We also show that black hole ringdown can serve as a realistic observational probe of this idea and may provide quantitative upper bounds on the strength of horizon-induced inner product flux. In this way gravity, entropy production, and effective non-Hermiticity are unified under a single structural principle, with Hermiticity emerging as the special case of globally conserved inner product symmetry.

We investigate the propagation of two-point density correlations in the one-dimensional Bose-Hubbard Hamiltonian in the thermodynamic limit in terms of the correlation transport distance (CTD), an experimentally measurable magnitude that characterizes the spatial spreading of correlations in time. We confirm that the integrable limits of the model exhibit CTD ballistic growth, while the onset of the chaotic phase leads to the emergence of a pronounced sub-ballistic regime, in agreement with previous results for finite systems. By a meticulous analysis of the spatio-temporal correlation profiles, we show that the correlation front nonetheless propagates ballistically for all interaction strengths, and that the chaos-induced slowdown of the CTD originates from the emergence of long-time distance-dependent correlation tails, together with an enhanced decay of the correlation front amplitude. Our results thus provide a detailed characterization of correlation transport that goes beyond a simple light-cone picture.

Quantum simulations on current NISQ hardware are limited by its noisy nature, making efficient quantum error mitigation methods highly demanded. In this paper we introduce a novel mitigation scheme, applicable to arbitrary quantum simulations of time-dependent Hamiltonian dynamics on NISQ devices. The scheme uses a polynomial subset of extended qubit Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy equations as a sampling criterion of possible mitigated candidates for the quantum observables. We show that for favorable Hamiltonians the polynomial subset of BBGKY hierarchy equations leads to a polynomial overhead in both classical and quantum resources. We employ the method to mitigate simulations of the chiral magnetic effect (CME), a chiral feature of the Schwinger model. We empirically show the effectiveness of our scheme at recovering the real-time dynamics of the CME from noisy quantum simulations of the Schwinger model, for a range of different parameter values of the model. We numerically demonstrate a systematic reduction of quantum noise, together with an increasing noise reduction capability as the amount of BBGKY constraints grows.

The modified Langevin noise formalism (MLNF) models the interaction of the quantized electromagnetic field with an arbitrary lossy magneto-dielectric object placed in vacuum using three types of non-interacting bosonic polaritons: scattering, electric, and magnetic. These respectively represent free-space photons scattered by the object, and photons radiated by quantized electric and magnetic dipolar sources embedded within its volume. Recently [A. Ciattoni, Phys. Rev. A 110, 013707 (2024)], this formalism was justified from the canonical quantization of macroscopic electromagnetism (CQME) [Philbin, New J. Phys. 12, 123008 (2010)] in the Heisenberg picture. This was achieved by identifying the polariton operators within the formal solution of the macroscopic Maxwell equations, assuming they obey bosonic commutation relations to retrieve the canonical ones, and showing they diagonalize the CQME Hamiltonian. However, the explicit functional dependence of these polaritons on the underlying canonical field operators remained undetermined. In this paper, we derive the exact analytical expressions for the polariton operators in terms of the canonical CQME field operators. Using these mappings, we provide a direct and rigorous derivation of the MLNF from the canonical theory in the Schrödinger picture. Our derivation is structured in three foundational steps: 1) adopting the derived analytical expressions as the constitutive definitions of the polariton operators; 2) mathematically proving that these operators are strictly bosonic as a direct consequence of the canonical commutation relations; and 3) demonstrating that they exactly diagonalize the macroscopic CQME Hamiltonian.

We investigate the intrinsic ambiguity in the definition of correlation functions arising from the inevitable invasiveness of quantum measurements. While algebraic correlations defined as expectation values of products of observables are widely used, their relationship to operational ones defined through actual measurement procedures remain unclear. We demonstrate that the differences among various definitions of correlation functions and those among their underlying (quasi-)joint probability distributions are bounded above by a quantitative measure of measurement invasiveness. We further obtain a lower bound on the discrepancy among operational and algebraic (quasi-)joint probability distributions, providing a new form of the uncertainty relation. In addition, we identify an equivalence condition under which operational and algebraic correlations coincide. As an application, we analyze the quantum violation of the Leggett-Garg inequality and clarify the structural origin of the equivalence among different approaches to observing the violation, including sequential projective measurements and weak-measurement. Our results provide an operational foundation for the commonly used algebraic concepts of quantum theory.

We study the occurrence of long-lived metastable states in the 4f$^{13}$5d6s electron configuration of Yb$^+$. By optical pumping of a single trapped ion on the $^2F^\text{o}_{7/2}\rightarrow (7/2,0)_{7/2}$ transition at 377.5 nm, we prepare a wide range of metastable electronic states. We use a co-trapped control ion to sympathetically cool the spectroscopy ion, allowing us to accurately time its subsequent decay. We record a strong decay signal corresponding to a lifetime of 0.92(8) s, a weaker decay signal with lifetime 9.8(+2.9, -2.0) s, and find evidence for a much longer lifetime, $>$ 30 s. We identify the metastable states with these lifetimes qualitatively, and corroborate our results with atomic structure calculations that support the observed lifetimes and decay paths. These long-lived states provide new opportunities in qubit and qudit state detection and optical clocks.

We highlight recent progress in the study of artificial flat band systems with a threefold focus. First, we discuss single-particle flat band physics, which has advanced through the design of various flat band generators. These generators rely on the classification of flat bands in terms of compact localized states - their fundamental building blocks. A related development is the complete real-space description of flat band projectors. Next, we review studies on perturbations of flat bands, which provide new insights into the effects of disorder and, more importantly, the intricate interplay between many-body interactions and flat band physics. Finally, we survey the growing number of experimental realizations of flat bands across diverse physical platforms.

Coherent control of motional degrees of freedom of ultracold atoms in optical lattices offers a promising route towards programmable quantum dynamics with massive particles. We propose and analyze a scheme for implementing coherent rearrangement of ultracold atoms, corresponding to arbitrary unitary transformations on single-particle motional states. Exploiting an analogy between dynamics in optical superlattices and discrete linear optics, we employ the Clements scheme to systematically construct any global $N$-dimensional single-particle unitary from tunneling and phase shifts in arrays of double wells. Tunneling is controlled globally, while local operations are achieved through site-resolved potential shifts. We numerically investigate the susceptibility of the scheme to intensity noise and addressing crosstalk. We identify key subroutines enabled by this unitary construction, including the Discrete Fourier Transform and the implementation of non-native Hamiltonians. Extending the scheme to two dimensions enables all-to-all atomic rearrangement with a circuit depth that scales sublinearly with the atom number, providing a high-density and highly scalable approach to atom rearrangement.

Despite the robust security guarantees of Quantum Key Distribution (QKD), its practical deployment is significantly challenged by the dynamic nature of quantum channels and the complexity of real-time parameter optimization. In this paper, we propose OptiQKD, a protocol-agnostic machine learning framework specifically engineered to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) for the BB84, E91, and COW protocols. OptiQKD integrates Temporal Convolutional Networks (TCNs) for high-accuracy and short-horizon forecasting of channel-state fluctuations with a Reinforcement Learning (RL) controller for autonomous and real-time parameter selection. This optimization stack is strictly constrained by standard composable-security assumptions to ensure that performance gains do not compromise the underlying quantum security. We evaluate the framework by simulating critical environmental stressors, including depolarizing and amplitude-damping noise, under realistic device constraints, including channel loss, detector efficiency, and dark counts. Our results demonstrate substantial protocol-agnostic improvements: the median SKR increases by 20--30%, while the median QBER is reduced from 3.0% to 1.5% through predictive state optimization. These findings establish that OptiQKD provides an efficient, security-preserving mechanism for dynamic parameter tuning, paving the way for more resilient and high-throughput practical QKD deployments.

This paper proposes a distributed computing framework for solving the Lindblad master equation in large-dimensional cavity QED systems. By leveraging the sparsity of the jump operator and combining this approach with the Cannon algorithm, the computational complexity of non-unitary terms is reduced from $O(MN^3)$ to $O(MN)$. For unitary terms, a combination of Taylor series approximation and the Cannon algorithm enables distributed matrix exponentiation, though scalability is limited by cross-processor communication. The proposed dynamic subspace construction method further reduces the Hamiltonian dimension: when $n_{\text{at}}=10$, the dimension is reduced to $5.63\%$ of the full Hamiltonian, with a memory footprint of only $0.32\%$. Results show that this framework significantly accelerates non-unitary evolution, providing a feasible solution for simulating large-scale open quantum systems where the number of dissipative channels $M$ is much larger than the Hamiltonian dimension $N$.

The property of non-stabiliserness, or ``magic'', is of interest in quantum computing due to its role in developing fault-tolerant quantum algorithms with genuine computational advantage over classical counterparts. There has been much interest in quantifying magic in various physical systems, in order to probe how to produce and enhance it. The production of magic has previously been quantified in gluon and graviton scattering, in the so-called helicity basis relating particle spins with momentum directions. For a basis-independent statement, one should instead use the recently developed concept of non-local non-stabiliserness, and our aim in this paper is to derive how this varies for gluon and graviton scattering processes. Our results show that, for many initial states, including those produced with polarised beams, the helicity basis coincides with a basis in which the non-local magic is manifest, providing a physical motivation for using the helicity basis to study quantum information quantities. However, this property breaks upon adding additional operators to the Yang-Mills Lagrangian, as would be the case in new physics scenarios.

We study quantum entanglement in a system comprising two quantum dots interconnected through the short topological superconducting nanowire, which hosts overlapping boundary Majorana modes. Inspecting the fermionic negativity, we analyze the variation of entanglement against the position of the energy levels of quantum dots and their hybridization with the topological superconducting nanowire. In the absence of electron correlations, the optimal entanglement occurs when the energy levels coincide with the zero-energy Majorana modes, whereas upon increasing the hybridizations, the entanglement is gradually suppressed. Such monotonous behavior is no longer valid when the quantum dot levels are detuned from the zero-energy. Under these circumstances, the quantum dots become maximally entangled for a certain optimal hybridization. Moreover, we study the thermal concurrence to explore the entanglement properties at finite temperatures. We also compute the quantum mutual information and propose recipes for robust finite-temperature entanglement transmission via Majorana modes.

As global demand for food and clean energy intensifies, agrivoltaic systems have emerged as a vital solution for land-use optimization. However, current designs overwhelmingly treat incident light as a classical photon flux, overlooking the quantum mechanical nature of photosynthetic energy transfer. We introduce spectral bath engineering-the strategic spectral filtering of sunlight through semi-transparent organic photovoltaic (OPV) panels to exploit non-Markovian quantum coherence in biological light-harvesting. Using Process Tensor HOPS (PT-HOPS) and Spectrally Bundled Dissipators (SBD) to simulate the Fenna-Matthews-Olson complex, we demonstrate that selective filtering at vibronic resonance wavelengths (750nm and 820nm) enhances the electron transport rate (ETR) by 25% relative to standard Markovian models. This quantum advantage is driven by vibronic resonance-assisted transport, which extends coherence lifetimes by 20% to 50% and nearly doubles pairwise concurrence (89%). Multi-objective Pareto optimization identifies OPV configurations reaching 18.8% power conversion efficiency while sustaining an 80.5% system ETR, potentially generating an additional USD 470 to 3000 $ha^{-1}$$yr^{-1}$ in revenue. Environmental simulations across nine climate zones, including sub-Saharan Africa, confirm persistent ETR enhancements of 18% to 24%. Finally, eco-design analysis using quantum reactivity descriptors ensures that these technological gains are achieved using sustainable, biodegradable materials. By bridging quantum biology and renewable energy engineering, this work provides a quantitative blueprint for next-generation agrivoltaic materials that co-optimize agricultural productivity and energy yield.

The Steiner Tree Problem (STP) is a well-known NP-hard combinatorial optimization problem, which has wide applications in network design, integrated circuit layout, bioinformatics, and other fields. However, traditional algorithms often struggle to balance efficiency and solution quality when dealing with large-scale STP instances. In this paper, we propose a new quantum annealing-based algorithm for solving the STP: we first model the STP into a quadratic unconstrained binary optimization (QUBO) form suitable for quantum annealing, then design a corresponding encoding strategy, and finally verify the algorithm through experimental tests. The results show that our quantum annealing-based method can obtain high-quality solutions with relatively low computational overhead for moderate-scale STP instances, providing a new feasible path for handling this intractable combinatorial optimization problem.

Van der Waals (vdW) ferroelectrics are emerging nonlinear photonic materials that combine large second-order susceptibility \c{hi}(2) with heterostructure compatibility, offering an attractive route toward miniaturized spontaneous parametric down-conversion (SPDC) sources. However, vdW SPDC sources operating under continuous irradiation in air remain limited in low brightness and poor operational stability, as oxygen and moisture exposure, together with pump-induced heating, lead to material degradation and permanent damage. Here we demonstrate an air-stable, bright SPDC source based on ferroelectric NbOI2 enabled by graphene encapsulation. Graphene provides robust environmental protection and can effectively supress pump induced degradation by enhancing heat dissipation. We report a record photon-pair generation absolute rate of 258 Hz and a normalized brightness of 19,900 Hz/(mW.mm). Leveraging this stabilized platform, we further generate polarization entangled photon pairs with 94% fidelity with respect to the maximally entangled Bell states from graphene-encapsulated 90° twisted bilayer NbOI2. Our results establish a practical and air-stable vdW ferroelectric SPDC platform that overcomes key limitations of existing vdW quantum light sources and provides a viable pathway toward scalable, integrated entangled photon sources for on chip quantum photonics.

It is mandatory to know how to operationally define and translate a reference frame into mathematics, in order that a physical interpretation of theory calculations in terms of observational data is possible. The situation is particularly challenging for gauge systems such as General Relativity where spacetime coordinates are subject to spacetime diffeomorphisms considered as gauge transformations turning coordinates into non-observables. This motivates the idea of operationally defined (material) reference frames which specify coordinates in terms of matter or geometry reference fields leading to the concept of relational observables, relational reference frames and gauge reduction. Upon quantisation, all fields become operator valued distributions. Now new conceptual and technical questions arise such as: Should one reduce before or after quantisation and how are the reference fields quantised respectively in either route? Is a reference frame itself subject to quantisation and how are different quantum reference frames related? How does the gauge reduction fit into this, i.e. how can it be that a certain reference field is considered a non-observable in one reference frame and an observable in another which upon quantisation even displays fluctuations? How precisely are gauge dependent fields interpreted in terms of the relational observables in a given reference frame? What is the relative dynamics, e.g. how exactly are physical Hamiltonians of two relational reference frames related? The present conceptual work addresses these and related questions in a non-perturbative field theory context of sufficient generality to cover General Relativity coupled to standard matter. A central role is played by the concept of the relational reference frame transformation (RRFT) for which a general formula is derived and its properties are explored.

Physical quantum systems are generically coupled to an environment, resulting in open system dynamics. A typical approach to simulating this dynamics is to propagate the density matrix of the system via the Lindblad master equation. This approach is numerically challenging due to the size of the density matrix, which has led to the development of quantum jump methods, which unravel the density matrix into an ensemble of state vectors. These methods utilize a stochastic sampling of the quantum jump times, which becomes inefficent for weakly dissipative dynamics, in which jumps are rare events. Here, we propose the deterministic quantum jump (DQJ) method, which we show to outperform standard quantum jump methods in the weakly dissipative regime, by removing the error of stochastic sampling. We describe the methodology at the single-jump and two-jump level, reconstructing the density matrix at the corresponding level. We demonstrate the performance of the method for two examples, the dissipative transverse-field Ising model, and the dissipative Kerr oscillator. Given that quantum technologies such as quantum computing have weakly dissipative quantum dynamics as their central focus, we propose this method to be utilized in that context, for exploring and understanding quantum technology platforms.

Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis testing, wherein the goal is to perform an optimal measurement for arriving at a correct conclusion. Mathematically, a measurement operator is Hermitian with eigenvalues in [0,1]. By noticing that this constraint on each eigenvalue is the same as that imposed on fermions by the Pauli exclusion principle, we interpret every eigenmode of a measurement operator as an independent effective fermionic mode. Under this perspective, various objective functions in quantum hypothesis testing can be viewed as the total expected energy associated with these fermionic occupation numbers. By instead fixing a temperature and minimizing the total expected fermionic free energy, we find that optimal measurements for these modified objective functions are Fermi-Dirac thermal measurements, wherein their eigenvalues are specified by Fermi-Dirac distributions. In the low-temperature limit, their performance closely approximates that of optimal measurements for quantum hypothesis testing, and we show that their parameters can be learned by classical or hybrid quantum-classical optimization algorithms. This leads to a new quantum machine-learning model, termed Fermi-Dirac machines, consisting of parameterized Fermi-Dirac thermal measurements-an alternative to quantum Boltzmann machines based on thermal states. Beyond hypothesis testing, we show how general semidefinite optimization problems can be solved using this approach, leading to a novel paradigm for semidefinite optimization on quantum computers, in which the goal is to implement thermal measurements rather than prepare thermal states. Finally, we propose quantum algorithms for implementing Fermi-Dirac thermal measurements, and we also propose second-order hybrid quantum-classical optimization algorithms.

Radiative-conductive systems are intrinsically nonlinear due to the quartic temperature dependence of thermal radiation. Under fixed total heating power, convexity arguments imply that nonuniform temperature distributions radiate more efficiently and therefore exhibit a lower mean temperature than their isothermal counterparts. However, this conclusion remains qualitative, and an explicit quantitative relation between temperature heterogeneity and mean temperature reduction has been lacking. Here we derive a variance-based analytical expression linking the area-averaged temperature to the corresponding isothermal equilibrium temperature in a nonuniformly heated radiative--conductive system. By integrating the governing equation and performing a systematic second-order expansion about the ambient temperature, we show that the decrease of the mean temperature relative to the isothermal equilibrium value is linearly proportional to the temperature variance, with a proportionality coefficient set solely by the ambient temperature. This result transforms the convexity-based inequality into a quantitative statistical relation within the perturbative regime and provides a physically transparent framework for describing nonlinear radiative averaging in thermally heterogeneous systems.

We present a semiclassical phase-space method to calculate thermal and ground states of large interacting spin systems. To this end, we extend the recently developed truncated Wigner approximation for spins (TWA) to the imaginary time, termed iTWA. The evolution of the canonical density matrix in imaginary time is mapped to a partial differential equation of its Wigner function. Truncation at the Fokker-Planck level leads to a set of stochastic differential equations, which can be efficiently simulated. We show that the iTWA can provide very good approximations to the ground state of a random and in general frustrated anti-ferromagnetic Ising Hamiltonian on a 3-regular graph, for which finding the exact ground state and approximations to it beyond a certain accuracy is NP hard. Furthermore in order to assess the ability of the method to properly account for leading-order quantum effects, we analyze the ground-state quantum phase transition of the nearest-neighbor, transverse-field Ising model in one and two spatial dimensions, finding very good agreement with the exact behaviour. The critical behavior obtained in iTWA follows the quantum-classical correspondence.

Quantum technologies rely on high-quality resource states, such as maximally entangled or private states, which are indispensable for quantum communication and cryptography. In practice, however, these states are inevitably degraded by noise. Distillation protocols aim to recover high-resource states from multiple imperfect copies, and while stabilizer-based methods have demonstrated high performance in entanglement purification, they have yet to be established for broader tasks such as secret-key distillation. This work introduces a unified framework for stabilizer-based resource distillation in systems of prime local dimension. By formulating stabilizer routines as quantum channels and deriving closed-form expressions for their output, we enable the application of stabilizer operations to general input states and diverse distillation objectives. We identify key invariances in resource measures, such as coherent and private information, and demonstrate how they can be leveraged to significantly reduce the numerical complexity of channel optimization. To illustrate the framework's versatility, we introduce several protocols: gF-IMAX for general fidelity optimization, and (S)CI-IMAX and (S)PI-IMAX for optimizing (smooth) coherent and private information in both asymptotic and one-shot regimes. Our numerical results confirm that these protocols effectively tailor stabilizer channels to specific operational tasks, establishing them as a robust and flexible tool for quantum resource distillation.

Following any quantum information processing protocol, it is essential to reset a mixed state of a many-body interacting spin-network to the computational-zero pure state. This task is challenging, both theoretically and experimentally, because of the quantum correlations. There is currently no effective cooling strategy for both high and low temperatures in such networks. Here we put forth a universal cooling strategy for multi-spin interacting networks. The strategy is based on the collective coupling of the system to an ancilla spin that intermittently dumps part of its entropy into an ultracold bath. Yet this strategy should overcome the symmetry-imposed correlations that impede the cooling. To avoid the prohibitive complexity of computing the dynamics, we resort to graph analysis of the network. %To approach the desired state, We show that a unique choice of alternating, non-commuting system-ancilla interaction Hamiltonians exists that breaks the symmetry constraints and allows the network to approach the desired pure state. We illustrate this universal purification strategy in diverse experimental settings.

We present a time-domain scheme for computing Casimir forces within the Maxwell stress tensor formalism, together with a specific realization using the finite-element-based discontinuous Galerkin time-domain method. The approach enables accurate evaluation of Casimir--Lifshitz interactions for a wide range of geometries and material properties at finite temperature. At the core of the method, the electromagnetic Green's tensor is expressed as the system's response to dipolar excitations, thereby recasting the Maxwell stress tensor into a set of classical scattering problems driven by electric and magnetic dipoles. We validate the approach against reference calculations of the Casimir interaction between parallel half-spaces at both zero and nonzero temperature. We further demonstrate its applicability to finite, cylindrically symmetric geometries for which closed-form solutions are unavailable, obtaining accurate agreement with asymptotic predictions based on physical considerations. These findings illustrate the method's potential for studying Casimir interactions in realistic micro- and nanoscale structures, relevant to nanodevice design and experimental settings.

The diversity of quantum correlations -- discord, entanglement, steering, and Bell nonlocality -- disappears at the observable second-moment kinematic level. By treating state purity as a finite resource, we introduce a local-unitary-invariant budget split of symmetrised second moments into local and nonlocal sectors that maps quantum systems onto compact, two-dimensional, hole-free manifolds. The topology of these manifolds is governed by state purity and time-reversal symmetry. This dimensional reduction reveals a deep structural link: exceeding classical capacity limits forces the activation of time-asymmetric generators, guaranteeing non-positive partial transpose entanglement. For two qubits, the geometry is analytically solvable. A single boundary elegantly isolates classical correlations, while nested regions physically dictate entanglement, steering, Bell nonlocality, and bounds on non-stabiliser magic. Beyond two qubits, dimensional capacity bottlenecks enforce these universal kinematic limits on correlation structures. Because this macroscopic representation is completely determined by global and marginal purities, it bypasses the exponential scaling of full-state tomography. By coarse-graining over gauge-like first moments, the budget geometry acts as a thermodynamic phase diagram, exposing both the static hierarchy of quantum resources and their dynamic redistribution under decoherence.

While quantum batteries have been widely studied under static driving, their performance under periodic driving in many-body systems remains far less understood. In this Letter, we uncover structural principles showing that many-body structure fundamentally determines the charging performance of a collective spin-1/2 quantum battery driven by a periodic Ising charger. In particular, interaction range, boundary conditions, system size, and integrability -- capturing graph connectivity, geometry, even-odd effects, and many-body dynamics -- emerge as critical factors for enhancing stored energy and charging power. First, we analyze how connectivity scaling and boundary geometry shape battery performance. We show that long-range interacting chargers exhibit superextensive energy storage, approaching the fundamental upper bound over broad ranges of driving periods and system sizes. In contrast, nearest-neighbor chargers achieve optimal charging only under finely tuned commensurability conditions. Moreover, we find that open boundary conditions (OBC) enhance robustness compared to periodic boundary conditions (PBC). Second, we examine the role of integrability under periodic driving. We demonstrate that nonintegrability enhances energy storage by suppressing conserved quantities and promoting ergodic Floquet dynamics, thereby enabling efficient population of the many-body spectrum. Through systematic structural optimization across multiple parameters, we identify long-range nonintegrability as a central resource for fast, scalable, and robust charging of collective quantum batteries. Our results clarify how structural features of many-body systems, together with periodic driving, can be harnessed to achieve efficient collective charging dynamics.

We investigate the emergence of fractional topological invariants in a periodic Su-Schrieffer- Heeger chain subject to gain and loss, governed by the Gorini-Kossakowski-Sudarshan-Lindblad master equations. After preparing the symmetry condition for integer topological invariants, we investigate their transition to fractional ones in steady states, which can happen either by tuning parameters in jump operators or as a dynamical transition during time evolution. Moreover, we show that these fractional topological invariants no longer possess quantized topology in the conventional sense. However, by extending the Brillouin zone to cover multiple cycles, the total winding regains integer quantization. Finally, we show how such effects can be observed in long-range hopping photonic lattices with fractional fillings, via Bloch state tomography. Our results open a new pathway to understand fractional topology in open quantum systems.

Federated Learning (FL) enables privacy-preserving distributed model training, yet remains vulnerable to gradient inversion and model leakage attacks. Quantum communication has been proposed to provide information-theoretic security for parameter aggregation. However, practical deployment is severely constrained by communication overhead and quantum channel noise. In this work, we present a systematic quantitative study of communication--convergence--noise trade-offs in Quantum Federated Learning (QFL). We introduce two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction based on light-cone feature selection in parametrized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches from centralized to decentralized aggregation during training. We derive explicit communication cost formulas and show that Hybrid QFL reduces quantum transmissions from $3NMP$ per round to $\{3t + 2(T - t)\}NMP$, achieving substantial savings while preserving near-centralized convergence. We further analyze robustness under depolarizing noise and show that decentralized aggregation is more noise-resilient because it transmits fewer qubits per round. Finally, we evaluate the effectiveness of Steane code-based quantum error correction under high-noise regimes. Our results provide an integrated design framework for communication-efficient and noise-aware QFL, clarifying practical trade-offs necessary for scalable quantum-secure distributed learning.

We analyse the dynamics of a qubit coupled to a dissipative impurity by comparing local and global derivation schemes of a Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation within the Born-Markov and full secular (FS) approximations. We show that the local approach correctly captures a crossover in the dynamics of the qubit coherence, while the FS approximation restricts the validity of the global approach to regimes with well-separated energy scales. Our results clarify the domains of validity of the two approaches and show that the local scheme provides a better GKSL description of the qubit dynamics in the experimentally relevant parameter regime.

We ask whether the Hamiltonian, interaction geometry, and symmetries of a quantum many-body system can be inferred from a few low-lying eigenstates without knowing which sites interact with each other. Directly solving the eigenvalue equations imposes constraints that yield a highly degenerate subspace of candidate operators, where the local Hamiltonian is hidden among an extensive family of conserved quantities, obscuring the interaction geometry. Here we introduce O-Sensing, a protocol designed to extract the Hamiltonian and symmetries directly from these states. Specifically, O-Sensing employs parsimony-driven optimization to extract a maximally sparse operator basis from the degenerate subspace. The Hamiltonian is then selected from this basis by maximizing spectral entropy (effectively minimizing degeneracy) within the sampled subspace. We validate O-Sensing on Heisenberg models on connected Erdős--Rényi graphs, where it reconstructs the interaction geometry and uncovers additional long-range conserved operators. We establish a learnability phase diagram across graph densities, featuring a pronounced ``confusion'' regime where parsimony favors a dual description on the complement graph. These results show that sparsity optimization can reconstruct interaction geometry as an emergent output, enabling simultaneous recovery of the Hamiltonian and its symmetries from low-energy eigenstates.

Amplitude encoding of real-world data on quantum computers is often the workflow bottleneck: direct amplitude encoding scales poorly with input size and can offset any speedups in subsequent processing. Fourier-based sparse amplitude encoding lowers cost by retaining only a small subset of dominant coefficients, but its fixed, non-adaptive basis leads to significant information loss. In this work, we replace the Fourier transform with the adaptive interpolating quantum transform (AIQT) in the sparse amplitude encoding workflow. The AIQT learns a data-adapted basis that concentrates information into a small number of coefficients. Consequently, at matched sparsity, the AIQT retains more information and achieves lower reconstruction error compared to the Fourier baseline. On financial time-series data, the AIQT reduces reconstruction error by 40% relative to the Fourier baseline, and on image datasets the reduction is up to 50% at the same sparsity level, with nearly identical encoding gate cost. Crucially, the approach preserves the efficiency of Fourier-based methods: the AIQT is built on the structure of the quantum Fourier transform circuit. Its gate count scales quadratically with the number of qubits, while classical evaluation can be carried out in quasilinear time. In addition, the AIQT is trained without labels and does not require sampling from quantum hardware or a simulator, removing a major bottleneck in data-driven amplitude-encoding methods.

We implement a variational quantum algorithm for Gibbs state preparation of a transverse-field Ising model on IonQ's quantum computers. To this end, we train the variational parameters via classical simulation and perform state tomography on the quantum devices to evaluate the fidelity of the prepared Gibbs state. As a main result, we find that fidelity decreases (non-monotonically) as a function of the inverse temperature $β$ of the system. Fidelity also decreases as a function of the size of the system. Interestingly, we find that a Gibbs state prepared for a specified $β$ is a better representative of a Gibbs state prepared for a $\textit{lower}$ $β$; or in other words, thermal fluctuations in the quantum hardware lead to digital heating, that is, an increase in the temperature of the prepared Gibbs state above what was intended.

Computing the vacuum and energy spectrum in non-Abelian, interacting lattice gauge theories remains an open challenge, in part because approximating the continuum limit requires large lattices and huge Hilbert spaces. To address this difficulty with near-term quantum computing devices, we adapt the variational quantum eigensolver to non-Abelian gauge theories. We outline scaling advantages when using a spin-network basis to simulate the gauge-invariant Hilbert space and develop a systematic state preparation ansatz that creates gauge-invariant excitations while alleviating the barren plateau problem. We illustrate our method in the context of SU(2) Yang-Mills theory by testing it on a minimal toy model consisting of a single vertex in 3+1 dimensions. In this toy model, simulations allow us to investigate the impact of noise expected in current quantum devices.

Given the rapid advances in quantum computing hardware, establishing systematic strategies for verifying the correctness of quantum computations has become increasingly important. Exploiting the fact that the axial anomaly in gauge theories is exact to all orders in perturbation theory, we propose the axial anomaly as a nontrivial benchmark for quantum simulations of lattice gauge theories. We simulate anomalous axial-charge production in ${\mathbb Z}_N$ lattice gauge theories on the trapped-ion quantum computer ``Reimei''. After taking the U(1), infinitesimal time, and infinite volume limits, we successfully reproduce the anomaly coefficient within statistical uncertainties, even without error mitigation. Our results demonstrate that the axial anomaly can be simulated on current quantum computers and serves as a verification test of quantum computations.

Quantum transducers are critical for quantum interconnect, enabling coherent signal transfer across disparate frequency domains. Beyond material and device advances, protocol design has become a powerful means to improve transduction. We introduce a variational quantum transduction (VQT) framework that employs variational tools from near-term quantum computing to systematically optimize protocol performance. As a variational quantum circuit framework, VQT is not plagued by known training issues such as barren plateau, because a small-scale problem is sufficient for substantial advantage and training only needs to be done once to configure a VQT system. Maximizing the quantum information rate within this framework yields protocols that surpass all known schemes in their respective classes. For non-adaptive protocols, VQT exceeds the performance envelopes of Gottesman-Kitaev-Preskill (GKP)-based and entanglement-assisted approaches. In the adaptive setting, VQT provides only a marginal improvement over Gaussian feedforward strategies, indicating that Gaussian adaptive transduction is already close to optimal. With increasingly universal quantum control, VQT provides a systematic path toward optimal quantum transduction.

We introduce Monarq, a unified quantum data processing framework that combines QCrank encoding with the EHands protocol for polynomial transformations, and demonstrate its implementation on noisy intermediate-scale quantum (NISQ) hardware. This framework provides fundamental quantum building blocks for signal and image processing tasks, including convolution, discrete-time Fourier transform (DFT), squared gradient computation, and edge detection, serving as a reference for a broad class of data processing applications on near-term quantum devices.

One of the primary challenges of photonic quantum information processing is the on-demand preparation of multiple single-photon-level quantum states from probabilistic photon pair sources. Motivated by recent developments in frequency-bin-encoded photonic quantum information processing, here we consider active time multiplexing to generate n-photon states, where n single photons with n distinct frequencies occupy the same spatiotemporal mode. We devise an approach that uses optical quantum memories to manipulate the temporal mode of heralded single photons and an array of fiber Bragg grating reflectors to jointly manipulate the frequency and temporal modes of the photons, overlapping n photons in n separate frequency bins into a single spatiotemporal mode. We calculate multiphoton state generation rates that, accounting for loss, are realistically achievable with commercially available hardware. Using only a single free-space switchable delay loop for an optical quantum memory, this scheme could feasibly produce 8-photon states at an average rate of 1 kHz.

Inspired by the formalism that relates the star-exponential with the quantum propagator for bosonic systems, in this work we introduce the analogous extension for the fermionic case. In particular, we analyse the problem of calculating the star-exponential (i.e., the symbol of the evolution operator) for Fermi systems within the deformation quantization program. Grassmann variables and coherent states are considered in order to obtain a closed-form expression for the fermionic star-exponential in terms of its associated propagator. As a primary application, a fermionic version of the Feynman-Kac formula is derived within this formalism, thus allowing a straightforward calculation of the ground state energy in phase space. Finally, the method is validated by successfully applying it to the simple harmonic and driven Fermi oscillators, for which the results developed here provide a powerful alternative computational tool for the study of fermionic systems.

The Sleeping Beauty Problem (SBP) is a long-standing puzzle in classical probability theory and has been used to challenge the Many-Worlds Interpretation (MWI) of quantum mechanics, since both involve objective determinacy combined with subjective uncertainty about certain events. A common concern is that MWI yields a different answer to the quantum version of SBP than the widely supported Thirder position in the classical case. We argue that this concern is unwarranted. We show that in both the quantum and classical versions of SBP, the correct credence is given by the Halfer position. In the quantum (MWI) SBP, we show that if no unjustified renormalization is introduced, the correct credence is 1/2. We then extend this result to the classical SBP by refuting four major arguments for 1/3. First, we reject the Proportion Argument by distinguishing event weight from probability. Second, we rebut Elga's Variant Argument by extending an earlier critique that identifies the implicit introduction of additional information; we further clarify this point by constructing a new variant and explaining why the Principle of Indifference is inapplicable, drawing an analogy with a mistake by d'Alembert in the history of probability theory. Third, we identify a flaw in the Technicolor Beauty Variant Argument, which arises from treating overlapping events as disjoint. Finally, we argue that causal decision theory is inappropriate for SBP, rendering the Thirders vulnerable to a Dutch Book. Our results support the consistency of MWI under the challenge posed by SBP and suggest that the dominant position on SBP needs careful reconsideration.

We study electron transport in a strained graphene sheet subjected to a sequence of $N$ electrostatic and magnetic barriers. Employing a modified and improved transfer-matrix framework, we examine how the transmission and reflection coefficients evolve with variations in uniaxial strain and in the number of barriers. The interplay of mechanical deformation and external magnetic fields is found to generate an anomalous Klein tunnelling, allowing the conductance to be effectively modulated through strain and barrier configurations. These findings highlight the role of strain engineering and magnetic field modulation as powerful tools for tailoring charge transport in two-dimensional materials. More broadly, they underscore how mechanical and electromagnetic control can be used to design next-generation solid-state devices with tunable electronic properties.

Multiparty quantum key agreement (MQKA) enables $n \geq 3$ mutually distrustful users to establish a shared secret key through collaborative quantum protocols. In this paper, we provide a comprehensive review where we argue that MQKA is best understood as a design space organized along three orthogonal but tightly coupled axes: (1) network architecture, which determines how quantum states flow between participants; (2) quantum resources, which encode the physical degrees of freedom used for implementation; and (3) security model, which defines trust assumptions about devices and infrastructure. Rather than treating MQKA as a linear sequence of isolated protocols, we develop this three-axis perspective to reveal recurrent patterns, sharp trade-offs, and unexplored design spaces. We classify MQKA protocols into structural families, map them to underlying quantum resources, and analyze how different security models shape fairness and collusion resistance. We further identify open challenges in composable security frameworks, network native integration, device-independent implementations, and propose a research roadmap toward hybrid-resource, bosonic-code-encoded, and fairness-aware MQKA suitable for the future quantum internet deployments in the post-NISQ era.

Detector dead time is typically treated as a fixed parameter in quantum key distribution (QKD) security analyses. In practice, however, the effective recovery time of single-photon avalanche photodiodes (SPADs) depends on the incident count rate. In this work, we demonstrate that this count-rate-dependent recovery nonlinearity constitutes a distinct attack primitive. We experimentally characterize the dead time shift of a free-running SPAD under controlled broadband loading and observe a substantial increase in effective recovery time as the detected rate rises into the high photon count regime. We show that recovery-induced availability reduction can be modeled as an adversarial erasure channel and derive a conservative bound on the signal detection probability under loading. Unlike previously studied detector-control or efficiency mismatch attacks, the proposed mechanism does not rely on deterministic blinding or timing discrimination. Instead, count-rate-dependent recovery asymmetry induces basis-dependent suppression of detection probabilities ($p_\perp<p_\parallel$), converting mismatch-induced errors into loss. Particularly, we show in active-basis BBM92 systems, this effect reduces the observed quantum bit error rate (QBER) below the abort threshold while increasing erasure probability. Using experimentally measured detector recovery data, we quantify the parameter regime in which such stealth suppression is achievable. These results establish count-rate-dependent detector recovery as a security-relevant vulnerability and show that countermeasures designed for timing-based efficiency mismatch do not directly address recovery-induced erasure (RIE) attack. Our findings underscore the need to incorporate detector recovery dynamics explicitly into practical QKD security models.

Semiconductor quantum dots host a rich manifold of excitonic complexes, including neutral excitons that emit anti-bunched single photons and charged exciton complexes capable of producing super-bunched photons via cascade emission. Accessing both emission regimes from a single emitter would open routes to novel quantum protocols, including advanced quantum imaging. In practice, however, emission from charged exciton complexes is intrinsically weak, often orders of magnitude dimmer than neutral excitons, placing simultaneous dual-mode operation out of reach. Here, we overcome this limitation by embedding the quantum dot in a dielectric Mie-resonant metasurface that provides order-of-magnitude photoluminescence enhancement across both neutral and charged exciton transitions of a single GaAs quantum dot. Under identical non-resonant pumping conditions, the emission from the neutral exciton yields anti-bunched emission ($g^{(2)}(0) < 0.5$) and the emission from positively charged exciton complexes shows super-bunched emission ($g^{(2)}(0) > 3.5$) with comparable count rates (~12 kHz). Crucially, super-bunching emerges only when charged exciton emission spectrally overlaps with the Mie resonances and vanishes in un-patterned slabs, demonstrating that photonic engineering, is essential for accessing these weak quantum light states. These results demonstrate a scalable, position-tolerant platform for harnessing the full excitonic structure of solid-state emitters.

Quantum non-Gaussian states and operations serve as fundamental resources for universal quantum computation, error correction, and high-precision metrology, extending beyond the Gaussian limits. While the stellar rank provides a rigorous hierarchical measure of non-Gaussianity, it remains challenging to determine experimentally. Conversely, witnesses of non-Gaussian features, based on the expectation values and variances of measurable observables, offer an accessible method for certifying non-Gaussian behavior but lack a direct connection to stellar rank. In this work, we establish a quantitative connection between these witnesses and stellar rank, demonstrating that the former can provide certifiable lower bounds on stellar rank. We introduce normalized expectation value and variance-based quantifiers and show that these witnesses form a consistent hierarchy of thresholds corresponding to stellar rank. Our results bridge the gap between abstract hierarchical measures and experimentally accessible quantifiers, enabling scalable certification of non-Gaussian states.

Coherent detection of ultralight bosonic dark matter can be achieved by monitoring slow Rabi oscillations in superconducting qubits. We introduce an ancilla-assisted, gate-based protocol that enhances sensitivity to the hidden photon kinetic mixing parameter $ε$ using a single two-qubit gate, bypassing the need to maintain long-lived multi-qubit entangled states and remaining compatible with the limitations of modern quantum hardware. We characterized the increase in sensitivity accounting for decoherence, thermal occupation, errors in readout and reset, indicating up to a ten-fold reduction in the required integration time to reach the same exclusion limit on $ε$ achievable via Rabi-sampling experiments. Under plausible hardware assumptions and three years of data taking, the projected $95\%$ C.L. exclusion limit on the hidden photon mixing parameter reaches $ε\approx 1\times 10^{-14}$ across $2.5$-$6.0$ GHz ($10$-$25$ \textmu eV).

In recent years, neutral atom quantum computers (NAQCs) have attracted a lot of attention, primarily due to their long coherence times and good scalability. One of their main drawbacks is their comparatively time-consuming control overhead, with one of the main contributing procedures being the detection of individual atoms and measurement of their states, each occurring at least once per compute cycle and requiring fluorescence imaging and subsequent image analysis. To reduce the required time budget, we propose a highly-parallel atom-detection accelerator for tweezer-based NAQCs. Building on an existing solution, our design combines algorithm-level optimization with a field-programmable gate array (FPGA) implementation to maximize parallelism and reduce the run time of the image analysis process. Our design can analyze a 256$\times$256-pixel image representing a 10$\times$10 atom array in just 115 $μ$s on a Xilinx UltraScale+ FPGA. Compared to the original CPU baseline and our optimized CPU version, we achieve about 34.9$\times$ and 6.3$\times$ speedup of the reconstruction time, respectively. Moreover, this work also contributes to the ongoing efforts toward fully integrated FPGA-based control systems for NAQCs.

Predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a critical bottleneck in drug discovery. While molecular fingerprints effectively capture local structural features, they struggle to represent higher-order correlations among molecular substructures. We present a quantum-inspired feature extraction method that encodes molecular fingerprints into a parameterized Hamiltonian, using mutual information (MI) to guide entanglement structure. By simulating quantum evolution on GPU-accelerated backends, we extract expectation values that capture pairwise and triadic correlations among fingerprint bits. On ten Therapeutic Data Commons (TDC) ADMET benchmarks, our method achieves state-of-the-art performance on CYP3A4 substrate prediction (AUROC 0.673 0.004) and improves over classical baselines on 8/10 tasks. SHAP (SHapley Additive exPlanations) analysis reveals that quantum-derived features contribute up to 33% of model importance despite comprising only 1.6% of features, demonstrating that Hamiltonian encoding concentrates predictive signal. This simulation study establishes the foundation for hardware validation on near-term quantum devices.

We show that monogamy of mutual information (MMI) in free-fermion ground states is a property of the observation scale, not of the quantum state. For three adjacent strips of width $w$ on a two-dimensional lattice, translation invariance decomposes the tripartite information as $I_3 = \sum_{k_y} g(k_F(k_y)\, w)$, where $g(z)$ is a universal function of the dimensionless product $z = k_F w$, determined by the spectrum of the sine-kernel integral operator (the Slepian concentration operator). We prove that $g(z)$ has a unique zero at $z^* \approx 1.329$: modes with $k_F w < z^*$ violate MMI ($g > 0$), while modes with $k_F w > z^*$ satisfy it ($g < 0$). Since $z^* / k_F w \to 0$ as $w \to \infty$, any Fermi surface eventually satisfies MMI at large $w$, while any gapless system violates it at sufficiently small $w$. The classification of states as "holographic" or "non-holographic" by the sign of $I_3$ is thus scale-dependent. We establish the properties of $g(z)$ analytically and show that $z^*$ is determined to $0.12\%$ by the cancellation of only two Slepian eigenvalue contributions. For Rényi entropies with index $α> 1$, the function $g_α(z)$ oscillates with multiple sign changes. We verify the framework on square and triangular lattices and show that interactions shift $z^*$ by $\sim 1$--$2\%$.

Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone does not guarantee this feature-learning capability. We study this question in the pure-state setting by viewing encoded data as an embedded manifold in $\mathbb{C}P^{2^n-1}$ and analysing infinitesimal unitary actions through Lie-algebra directions. We introduce Classical-to-Lie-algebra (CLA) maps and the criterion of almost Complete Local Selectivity (aCLS), which combines directional completeness with data-dependent local selectivity. Within this framework, we show that data-independent trainable unitaries are complete but non-selective, i.e. learnable rigid reorientations, whereas pure data encodings are selective but non-tunable, i.e. fixed deformations. Hence, geometric flexibility requires a non-trivial joint dependence on data and trainable weights. We further show that accessing high-dimensional deformations of many-qubit state manifolds requires parametrised entangling directions; fixed entanglers such as CNOT alone do not provide adaptive geometric control. Numerical examples validate that CLS-satisfying data re-uploading models outperform non-tunable schemes while requiring only a quarter of the gate operations. Thus, the resulting picture reframes QNN design from state reachability to controllable geometry of hidden quantum representations.

The complexity of highly excited eigenstates is a central theme in nonequilibrium many-body physics, underpining questions of thermalization, classical simulability, and quantum information structure. In this work, considering the paradigmatic Rokhsar-Kivelson model, we connect quantum many-body scarring in Abelian lattice gauge theories to an emergent stabilizer structure. We identify a distinct class of scarred eigenstates, termed sublattice scars, originating from gauge-invariant zero modes that form exact stabilizer states. Remarkably, although the underlying Hamiltonian is not a stabilizer Hamiltonian, its eigenspectrum intrinsically hosts exact stabilizer eigenstates. These sublattice scars exhibit vanishing stabilizer Rényi entropy together with finite, highly structured entanglement, enabling efficient classical simulation. Exploiting their stabilizer structure, we construct explicit Clifford circuits that prepare these states in a two-dimensional lattice gauge model. Our results demonstrate that the scarred subspace of the Rokhsar-Kivelson spectrum forms an intrinsic stabilizer manifold, revealing a direct connection between stabilizer quantum information, lattice gauge constraints, and quantum many-body scarring.

We employ a quantum computer to simulate the effect of spin impurities on nitrogen-vacancy (NV) centers in diamond. As these defects operate as nanoscale quantum sensors, modeling quantum noise is crucial to identify limitations in precision. The analysis is performed by means of quantum state tomography on two transmon qubits, representing respectively the NV center and a single spin impurity, modeling either a nuclear spin or an additional NV center. We demonstrate a versatile platform to simulate benchmark protocols such as Ramsey or Hahn-echo. Although we focus on a two-spin system, the same approach opens the door to using quantum processors as scalable simulators of many-spin environments, intractable in classical simulation due to the rapid exponential growth of the Hilbert space. The results reveal the effect different spin-sensor coupling regimes have on coherence, helping to identify detection schemes that maximize the sensitivity under the effect of impurities. Moreover, the role of entanglement generation is analyzed using the Peres-Horodecki criterion and CHSH inequalities. Although no violation of the latter is observed, the presence of entanglement is confirmed.

We report on the observation of motion-induced directionality in the collective emission of atoms confined within a hollow-core waveguide. Unlike in chiral waveguides, the atom-field coupling is here isotropic in the forward and backward direction. However, Raman-induced effective two-level emitters with spatially oscillating phases of the transition dipole enable thermally induced, but controllable directionality of the collective emission. By tuning the characteristic rate of collective decay we achieve a directionality of up to 0.89(1). We furthermore study the correlations of the emitted light close to and well above the threshold to collective emission, showing a buildup of coherence in the superfluorescent bursts while exhibiting thermal statistics below the threshold. To understand the underlying mechanism we employ numerical simulations based on the Truncated Wigner Approximation for spins and find good agreement. Additionally we present a simple model capable of reproducing the observed directionality via location blurring induced by the thermal motion of the atoms during collective emission. Our results will enable studies of collective, nonreciprocal interactions in non-chiral systems.

A promising way to scale up superconducting quantum computers is to link different devices together using propagating photons. Correspondingly, accurately modeling the quantum information transfer in such quantum interconnects is critical to advancing this emerging technology. To accomplish this, a full-wave quantum numerical model is essential for describing the few-photon transport characteristics of various components. Unfortunately, validating the accuracy of such numerical models remains a difficult challenge due to the lack of appropriate analytical solutions for standard component types. Recently, progress has been made on creating the first-ever analytical quantum full-wave solutions for a superconducting circuit quantum device. These efforts considered the case of two-photon transport through an empty rectangular waveguide cavity and the interactions of photons inside a closed rectangular waveguide cavity with a transmon qubit formed by a Josephson junction connected across the terminals of a small wire dipole antenna. Here, we advance these efforts by considering the one- and two-photon transport properties through a rectangular waveguide cavity containing a qubit in this form when the cavity is interfaced with via two coaxial ports. Such devices can be used in various ways for quantum interconnects, such as to form parts of a quantum memory or a photon source. We perform this analysis leveraging a quantum input-output theory formalism to derive the relevant single- and two-photon transport characteristics of interest. We then examine the signatures of the nonlinear quantum scattering effects in the good and bad cavity regimes of cavity quantum electrodynamics. In the future, these analytical results can be used to validate numerical full-wave quantum solvers for modeling quantum interconnects.

Quantum Computing promises to solve complex combinatorial optimization problems more efficiently than classical methods, with the Quantum Approximate Optimization Algorithm (QAOA) being a leading candidate. Recent fixed-parameter variations of QAOA eliminate costly run-time optimization, but determining their optimal initialization as well as the number of required layers (p) for a target solution remains a critical, unsolved challenge. In this work, we propose a novel approach using a Graph Neural Network (GNN) to predict QAOA performance: Based on a graph representation of the problem, the GNN forecasts the probability of the optimal solution in the resulting distribution across different parameter initializations and layer depths for a wide variety of combinatorial optimization problems. We demonstrate that the GNN accurately predicts QAOA performance within a 10% margin of the true values. Furthermore, the model exhibits strong generalization capabilities across unseen problem classes, larger problem sizes, and higher layer counts. Our approach allows to identify viable problem instances for QAOA and to select an adequate parameter initialization strategy with minimal layer depth, without the need of costly parameter optimization.

Interference is a universal consequence of superposition, yet in composite quantum systems it can encode correlations between subsystems. We show that in coupled electron-nuclear dynamics, interference in the nuclear density can arise dynamically even when it is initially absent. Starting from a superposition of orthogonal Born-Oppenheimer electronic states, we demonstrate within the exact factorisation framework that genuine non-adiabatic electron-nuclear correlations induce de-orthogonalisation of the electronic factors, thereby generating interference terms in the nuclear density. Such interference has no counterpart in adiabatic evolution. Unlike conventional nuclear wave-packet interference or interference that merely reflects electronic coherence in a chosen basis, the effect identified here is a manifestation of the compositeness of the full electron-nuclear state. Nuclear density interference thus emerges as a direct dynamical signature of correlated quantum motion in composite systems.

Variational quantum circuits for image classification suffer from barren plateaus, while quantum kernel methods scale quadratically with dataset size. We propose an iterative framework based on Quadratic Unconstrained Binary Optimization (QUBO) for training the classifier head of convolutional neural networks (CNNs) via quantum annealing, entirely avoiding gradient-based circuit optimization. Following the Extreme Learning Machine paradigm, convolutional filters are randomly initialized and frozen, and only the fully connected layer is optimized. At each iteration, a convex quadratic surrogate derived from the feature Gram matrix replaces the non-quadratic cross-entropy loss, yielding an iteration-stable curvature proxy. A per-output decomposition splits the $C$-class problem into $C$ independent QUBOs, each with $(d+1)K$ binary variables, where $d$ is the feature dimension and $K$ is the bit precision, so that problem size depends on the image resolution and bit precision, not on the number of training samples. We evaluate the method on six image-classification benchmarks (sklearn digits, MNIST, Fashion-MNIST, CIFAR-10, EMNIST, KMNIST). A precision study shows that accuracy improves monotonically with bit resolution, with 10 bits representing a practical minimum for effective optimization; the 15-bit formulation remains within the qubit and coupler limits of current D-Wave Advantage hardware. The 20-bit formulation matches or exceeds classical stochastic gradient descent on MNIST, Fashion-MNIST, and EMNIST, while remaining competitive on CIFAR-10 and KMNIST. All experiments use simulated annealing, establishing a baseline for direct deployment on quantum annealing hardware.

Accurate time-dependent quantum dynamics of Coulombic systems on grid-based representations remains computationally demanding due to the singularity of the Coulomb potential, which necessitates extremely fine spatial grids to mitigate discretisation errors. We propose two complementary correction schemes that, under identical resource budgets, consistently outperform the uncorrected counterparts. The first scheme modifies the potential operator to incorporate grid-basis structure into its representation, while the second introduces a corrected initial wavefunction inspired by analytical solutions of softened Coulomb potentials. Applied to hydrogenic systems, these corrections deliver improved energy accuracy and time fidelity across long evolutions. Beyond classical simulations, the proposed framework aligns naturally with quantum computing architectures, where the corrected operators and states can be encoded through truncated Walsh and Fourier series expansions. A resource analysis for the representative 2D hydrogen system yields a circuit depth of $1.5\times10^{8}$ gates over 6,000 Trotter steps. This study thus establishes practical strategies toward high-accuracy Coulombic dynamics on both classical and emerging quantum platforms.

Partial discharge originates from microscopic insulation imperfections in high-voltage apparatus and is widely considered a critical marker of incipient deterioration. Conventional partial discharge detection methods are typically constrained by limited bandwidth and often rely on predefined feature extraction, which impedes reliable recognition of broadband transient signals. In this work, we employ a Rydberg atomic sensor to directly capture time-domain responses of partial discharge emissions and construct distinctive spectral fingerprints for different types. A 1D ResNet deep learning model is then applied to recognize these fingerprints from time-domain signals without manual feature engineering. Under increased source-antenna distances, where spectral features are significantly attenuated, the model attains a recognition accuracy of approximately 94\% across four partial discharge categories, demonstrating robustness to attenuation and noise. We further validate the approach in a simulated early-warning scenario, where partial discharge signals mixed with noise are analyzed and the model successfully generates predictive alarms. These results underscore the potential of integrating Rydberg-based broadband sensing with data-driven analysis for non-invasive, high-sensitivity diagnostics of electrical insulation systems.

Most quantum communication networks around the world are used for a single task: quantum key distribution. In order to initiate the transition to multi-purpose quantum communication networks, we demonstrate the implementation of two different tasks on the same quantum key distribution hardware. Specifically, we focus on quantum oblivious transfer and quantum tokens. Our main contribution is to establish a methodology that greatly simplifies the expertise required to achieve the deployment, assess its performance, and evaluate its feasibility at a large scale. The implementation that we present is full-stack. It is based on a development framework that allows running user-defined applications both with simulated or real quantum communication backend. The hardware used for the implementation is VeriQloud's Qline. The simulation backend reproduces exactly the inputs and outputs of the real hardware, but also its losses and errors. It can therefore be used to validate the implementation before running it on the real hardware. The sources of the software that we use are fully open, making our research reproducible. The security of the implementations on real hardware are discussed with respect to security bounds previously known in the literature. We also discuss the engineering choices that we made in order to make the implementations feasible. By establishing a methodology to evaluate the performance and security of quantum communication protocols, we take a significant step towards industrializing and deploying large-scale, multi-purpose quantum communication networks.

We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure. Unlike purely data-driven supervised schemes, the proposed unsupervised autoencoder-based method incorporates the governing physics (here, the S-matrix formalism) within the decoder network, ensuring that the learned representations remain physically meaningful. Through numerical learning experiments, we demonstrate automated characterization of programmable solid-state simulators from transport measurements, exemplified by a triple quantum dot chain. The trained model generalizes beyond the training domain and accurately infers Hamiltonian parameters from transport data. While the model has finite capacity -- leading to degraded performance when the parameter space becomes excessively large or structurally diverse -- we identify regimes in which robust generalization is maintained. We further show how to train the model to handle noisy measurements, reflecting realistic experimental conditions.

The practical implementation difficulties arising from the Gaussian modulation of the GG02 protocol lead us to investigate the possibilities offered by the combination of probabilistic amplitude shaping technique and quadrature amplitude modulation formats in the context of continuous variable quantum key distribution systems. Our interest comes from the fact that quadrature amplitude modulation and probabilistic shaping can be implemented with current technologies and are widely used in classical telecom equipment. In this treatment, we assume to work in the scenario of a linear quantum channel and we analyze maximum achievable secure key rates, maximum reachable distances and the resilience to noise of our discrete-modulation based protocol with respect to GG02, which is taken as a benchmark. In particular, we deal with the infinite key size regime, consider a homodyne detection scheme, and analyze what happens for different cardinalities of the input alphabet at different distances, in the case of collective attacks and in the reverse reconciliation picture. We find that our protocol, beyond being easily reproducible in the laboratory, provides a way to closely approach the theoretical performance offered by GG02 and, at the same time, preserves the ability to assure an unconditional security level.

As quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of quantum network architectures and protocols. However, designing a scalable and computationally efficient quantum network simulator is intrinsically challenging: i) quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of entanglement, a quantum resource without any classical networking analog; ii) quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible quantum network simulator, built on top of ns-3, designed to seamlessly integrate quantum-network primitives with ns-3's established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving Quantum Internet scenarios. Q2NS natively supports multiple quantum state representations through a unified interface, allowing interchangeable state-vector, density-matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of entanglement dynamics and protocol behavior. Q2NS offers a flexible, open, and scalable simulation platform for advancing Quantum Internet research.

The rapid emergence of quantum technology has raised new challenges in distinguishing various quantum circuits of similar functions. In this work, we propose parallel quantum embedding neural network (ParaQuanNet) for the efficient identification of quantum generative circuits via classifications of the corresponding output data. Specifically, we generated W-like states with eight generative quantum circuits realizing the generative quantum denoising diffusion probabilistic models (QDDPM). Our ParaQuanNet can classify these eight classes of generated quantum data with an accuracy of {$99.5\%$}, even though all of them are trained to generate the same types of quantum data. With a novel design of parallel quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the quantum kernel circuit parallelly process all the receptive fields of quantum data, which empowers the quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for quantum circuits identification, contributing to the broader development of quantum machine intelligence.

Exploiting many-body interaction and critical phenomena to improve the performance of quantum batteries is an emerging and promising line of research. A central question in this direction is whether quantum phase transitions can enhance the charging energy or the power. While preliminary works have addressed this problem in fine-tuned integrable models, its characterization in non-integrable systems remains limited due to the demanding numerical requirements. Here, we investigate a one-dimensional Axial Next-Nearest-Neighbor Ising model as an example of non-integrable quantum battery charged via a quantum-quench protocol. In contrast to integrable cases, we find that criticality in this setting can lead to a pronounced enhancement of the charging power. Our findings inform quantum-battery design of many-qubit systems and are amenable to experimental verification on current quantum-simulation platforms, including neutral-atom arrays.

Quantum Kolmogorov--Arnold networks based on Chebyshev polynomials (CCQKAN) evaluate each edge activation function as a quantum inner product, creating a trade-off between qubit count and the number of circuit executions per forward pass. We introduce merged amplitude encoding, a technique that packs the element-wise products of all $n$ input-edge vectors for a given output node into a single amplitude state, reducing circuit executions by a factor of $n$ at a cost of only 1--2 additional qubits relative to the sequential baseline. The merged and original circuits compute the same mathematical quantity exactly; the open question is whether they remain equally trainable within a gradient-based optimization loop. We address this question through numerical experiments on 10 network configurations under ideal, finite-shot, and noisy simulation conditions, comparing original, parameter-transferred, and independently initialized merged circuits over 16 random seeds. Wilcoxon signed-rank tests show no significant difference between the independently initialized merged circuit and the original ($p > 0.05$ in 28 of 30 comparisons), while parameter transfer yields significantly lower loss under ideal conditions ($p < 0.001$ in 9 of 10 configurations). On 10-class digit classification with the $8\times8$ MNIST dataset using a one-vs-all strategy, original and merged circuits achieve comparable test accuracies of 53--78\% with no significant difference in any configuration. These results provide empirical evidence that merged amplitude encoding preserves trainability under the simulation conditions tested.

While real quantum devices have been increasingly used to conduct research focused on achieving quantum advantage or quantum utility in recent years, executing deep quantum circuits or performing quantum machine learning with large-scale data on current noisy intermediate-scale quantum devices remains challenging, making classical simulation essential for quantum machine learning research. However, classical simulation often suffers from the cost of gradient calculations, requiring enormous memory or computational time. In this paper, to address these problems, we propose a method to fuse multiple consecutive gates in each of the forward and backward paths to improve throughput by minimizing global memory accesses. As a result, we achieved approximately $20$ times throughput improvement for a Hardware-Efficient Ansatz with $12$ or more qubits, reaching over $30$ times improvement on a mid-range consumer GPU with limited memory bandwidth. By combining our proposed method with gradient checkpointing, we drastically reduce memory usage, making it possible to train a large-scale quantum machine learning model, a $20$-qubit, $1,000$-layer model with $60,000$ parameters, using $1,000$ samples in approximately $20$ minutes. This implies that we can train the model on large datasets, consisting of tens of thousands of samples, such as MNIST or CIFAR-10, within a realistic time frame (e.g., $20$ hours per epoch). In this way, our proposed method drastically accelerates classical simulation of quantum machine learning, making a significant contribution to quantum machine learning research and variational quantum algorithms, such as verifying algorithms on large datasets or investigating learning theories of deep quantum circuits like barren plateau.

We demonstrate the generation of $12.1 \pm 0.2$ dB squeezed light from a periodically poled lithium niobate (PPLN) waveguide optical parametric amplifier (OPA). While single-pass OPAs offer squeezed light with THz-order bandwidths, loss from spatial mode mismatch between the squeezed light and the local oscillator (LO) previously capped the squeezing level at $\sim$10 dB [K. Hirota et al., Opt. Express 34, 7958 (2026)]. In this work, we minimize this loss by introducing a machine-learning-optimized spatial light modulator (SLM) in the path of the LO. Specifically, we employed a double-reflection configuration to increase the spatial degrees of freedom, and directly used the measured squeezing level as the optimization's objective function.

We examine spin-resolved photoionization of randomly oriented chiral molecules via circularly polarized light, and revisit earlier predictions of Cherepkov (J. Phys. B: Atom. Mol. Phys. 16, 1543, 1983}). We will show that the dynamical origin of spin- and enantio-sensitive observables arise from two intrinsic mechanisms that are quantified by two pseudovectors stemming from the geometric properties of the photoionization dipoles in spin space and in real space, and an extrinsic mechanism which is a directional bias introduced by the well-defined direction of light polarization. These mechanisms arise solely from electric dipole interactions. Consequently, this means that the ten independent parameters that was earlier predicted by Cherepkov to fully describe spin-resolved photoionization of chiral molecules can be reduced as moments of these three pseudovectors. We also find that the molecular pseudoscalars describing the spin- and enantio-sensitive components of the yield can be described by the flux of these pseudovectors through the energy shell, which changes sign upon switching enantiomers. Our results provide compact expressions for these observables which provide an intuitive picture on what determines the strength of these spin- and enantio-sensitive observables. The approach can be readily generalized to photoexcitation, multiphoton processes, and arbitrary field polarizations. Regardless of the specific driving conditions, the resulting spin- and enantio-sensitive observables are still controlled by the same three pseudovectors, underscoring their universal role as the primary generators of chirality-induced spin asymmetries, emphasizing their fundamental geometric origin and the universality of the mechanism identified here.

The relationship between states obtained by the non-commutative integration method of the Schrödinger equation on Lie groups and generalized coherent states is investigated. It is shown that such solutions belong to the class of generalized coherent states when the corresponding λ-representation is real.

We propose a practical method for accurately evaluating molecular energies using a hybrid approach that integrates fault-tolerant quantum computers with classical computing. Our scheme comprises two complementary methods: quantum dominant orbital selection (QDOS) and subspace dynamical correlation (SDC). The QDOS method extracts only the relevant active orbitals from the complete active space (CAS) configuration interaction (CI) state on a quantum computer, thereby defining a more compact active space suitable for subsequent classical CASCI calculations. The SDC method evaluate correction of dynamical correlation of the CASCI obtained by quantum computing by using the compact CASCI state, which can be handled by classical computing. To demonstrate that the CAS energy resulting from the quantum computation is post-corrected by the SDC method, we examine the two frameworks, multi-reference perturbation theory and tailored coupled-cluster theory, for the SDC method. Our scheme does not suffer from massive task to read out quantum data readout and demonstrates the potential to efficiently compute large, complex molecular systems by leveraging quantum-classical hybrid computation with reasonable computational resources.

One-class classification (OCC) is a fundamental problem in machine learning with numerous applications, such as anomaly detection and quality control. With the increasing complexity and dimensionality of modern datasets, there is a growing demand for advanced OCC techniques with better expressivity and efficiency. We introduce Neural Quantum Support Vector Data Description (NQSVDD), a classical-quantum hybrid framework for OCC that performs end-to-end optimized hierarchical representation learning. NQSVDD integrates a classical neural network with trainable quantum data encoding and a variational quantum circuit, enabling the model to learn nonlinear feature transformations tailored to the OCC objective. The hybrid architecture maps input data into an intermediate high-dimensional feature space and subsequently projects it into a compact latent space defined through quantum measurements. Importantly, both the feature embedding and the latent representation are jointly optimized such that normal data form a compact cluster, for which a minimum-volume enclosing hypersphere provides an effective decision boundary. Experimental evaluations on benchmark datasets demonstrate that NQSVDD achieves competitive or superior AUC performance compared to classical Deep SVDD and quantum baselines, while maintaining parameter efficiency and robustness under realistic noise conditions.

Unitary t-designs are some of the most versatile tools in quantum information theory. Their applications range from randomized benchmarking and shadow tomography, to more fundamental ones such as emulating quantum chaos and establishing exponential separations between classical and quantum query complexity. While unitary designs originating from a group structure, such as the Clifford group, have proven to be incredibly useful for qubit systems, unfortunately, this is no longer true for qudits. In fact, the classification of finite-group representations rules out the existence of unitary 2-designs for arbitrary qudit dimensions. This severely limits the applicability of standard quantum information primitives when it comes to qudit systems. We overcome these limitations with a three-fold contribution. First, we introduce a general technique to construct families of weighted state t-designs in arbitrary qudit dimensions. These weighted state-designs generalize classical shadow tomography protocol from qubits to qudits. Second, we introduce a Clifford character RB that allows us to benchmark the qudit Clifford group in any dimension, including non-prime-power dimensions. And third, we establish bounds on the quantum circuit complexity of generating approximate unitary-designs from native gates in existing quantum hardware such as high-spin and cavity-QED qudits. Our work further highlights the analogy between spin and optical coherent states by proving that spin-GKP codewords form a state 2-design while spin coherent states do not; in direct analogy with the optical case. This work is structured as a pedagogical and self-contained introduction to unitary designs and their applications to qudit systems.

The graph isomorphism problem asks whether two graphs are identical up to vertex relabeling. While the exact problem admits quasi-polynomial-time classical algorithms, many applications in molecular comparison, noisy network analysis, and pattern recognition require a flexible notion of structural similarity. We study the quantum query complexity of approximate graph isomorphism testing, where two graphs on $n$ vertices drawn from the Erdős--Rényi distribution $\mathcal{G} (n,1/2)$ are considered approximately isomorphic if they can be made isomorphic by at most $k$ edge edits. We present a quantum algorithm based on MNRS quantum walk search over the product graph $Γ(G,H)$ of the two input graphs. When the graphs are approximately isomorphic, the quantum walk search detects vertex pairs belonging to a dense near isomorphic matching set; candidate pairings are then reconstructed via local consistency propagation and verified via a Grover-accelerated consistency check. We prove that this approach achieves query complexity $\mathcal{O}(n^{3/2} \log n/\varepsilon)$, where $\varepsilon$ parameterizes the approximation threshold. We complement this with an $Ω(n^2)$ classical lower bound for constant approximation, establishing a genuine polynomial quantum speedup in the query model. We extend the framework to spectral similarity measures based on graph Laplacian eigenvalues, as well as weighted and attributed graphs. Small-scale simulation results on quantum simulators for graphs with up to twenty vertices demonstrate compatibility with near-term quantum devices.

Quantum entanglement switches are a key building block for early quantum networks, and a central design question is whether near-term devices should use only flying photons or also incorporate quantum memories. We compare two architectures: an all-photonic entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing qubits, and a quantum memory-equipped switch that buffers entanglement and triggers measurements only when heralded connectivity is available (herald-then-swap control). These two designs trade off simple, memoryless operation that avoids decoherence and memory-induced latency against heralding-based control that buffers entanglement to use BSMs more efficiently. We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance. Numerical evaluation quantifies the rate-fidelity tradeoffs of both models, identifies operating regions in which each architecture dominates, and shows how hardware and protocol knobs can be tuned to meet application-specific targets.

Levitated macroscopic particles exhibiting quantum mechanical effects are garnering increased attention as a means for precision sensing and testing quantum mechanics. Defects in diamond, such as the nitrogen-vacancy (NV) centre possess optically-addressable spins with long coherence times at room temperature and offer an intriguing system to examine quantum spin dynamics coupled to a macroscopic classical particle. In this work, we convert the outcome of a quantum measurement on an ensemble of spins into a macroscopic rotation of the host particle via spin-mechanical coupling. Following a sequence of green laser and microwave control pulses, spin-mechanical coupling between the final qubit spin state and the host particle -- an electrically-levitated diamond -- exerts a torque on the particle that deflects a weak near-infra-red laser beam. We measure spin readout contrast in excess of 70\%, and demonstrate pulsed mechanical detection of coherent Rabi oscillations, spin-echo interferometry and $T_1$-induced relaxation. We directly measure with temporal resolution the particle reorientation from a 60\,attonewton-metre spin torque induced by flipping the spins. Our results open up interesting new opportunities for levitated spin-mechanical systems using pulsed control, from improved sensing to the prospect of realising macroscopic quantum superposition states.

High-stress silicon nitride (Si3N4) membranes represent the state-of-the-art for cavity optomechanics, combining ultralow dissipation, optical transparency, and full compatibility with wafer-scale nanofabrication. Yet their integration into high-finesse optical cavities has remained difficult, typically requiring bonding or alignment-sensitive assembly that limits scalability and long-term stability. Here, we introduce a monolithic, wafer-level integration strategy that directly suspends high-stress Si3N4 photonic-crystal membranes above thermally compatible SiN/SiO2 distributed Bragg reflectors (DBRs) capable of withstanding the high temperatures required for stoichiometric Si3N4 growth. A defect-free amorphous-silicon sacrificial layer and stiction-free plasma undercut yield vertically coupled cavities with sub-micron spacing-forming self-aligned resonators within seconds of release. Owing to the intrinsic tensile stress, the suspended membranes exhibit atomic-scale sagging, ensuring near-ideal cavity parallelism and long-term stability. Optical reflectivity measurements reveal cavity finesse exceeding 800 with nanoscale gaps between mirrors. Mechanical ringdown measurements show Q > 10^5, indicating that DBR integration preserves the low-dissipation character of high-stress Si3N4. This demonstrates that the integration process preserves the material's exceptional dissipation dilution, supporting straightforward extension to high-Q nanomechanical architectures reported in the literature. The resulting Si3N4-DBR platform unites optical and mechanical coherence with high fabrication yield and design flexibility, enabling scalable optomechanical devices for precision sensing and quantum photonics.

Due to fundamental limitations on terrestrial quantum links, satellites have received considerable attention for their potential as entanglement generation sources in a global quantum internet. In this work, we focus on the problem of designing a constellation of satellites for such a quantum network. We find satellite inclination angles and satellite cluster allocations to achieve maximal entanglement generation rates to fixed sets of globally distributed ground stations. Exploring two black-box optimization frameworks: a Bayesian Optimization (BO) approach and a Genetic Algorithm (GA) approach, we find comparable results, indicating their effectiveness for this optimization task. While GA and BO often perform remarkably similar, BO often converges more efficiently, while later growth noted in GAs is indicative of less susceptibility towards local maxima. In either case, they offer substantial improvements over naive approaches that maximize coverage with respect to ground station placement.

Under a time-dependent perturbation it is common to calculate the transition probability in going from from one eigenstate to another eigenstate of a quantum system. In this work we study the transition in going from a \textit{linear superposition of eigenstates} to an eigenstate under a delta-function pulse (which acts at $t=0$). We consider a two-level system with energy levels $E_1$ and $E_2$ and solve the coupled set of first order equations to obtain exact analytical expressions for the coefficients $c_1(t>0)$ and $c_2(t>0)$ of the final state. The expressions for the final coefficients are general in the sense that they are functions of the interaction strength $β$ and the coefficients $α_1$ and $α_2$ of the initial superposition state which are free parameters constrained only by $|α_1|^2+ |α_2|^2=1$. This opens up new possibilities and in particular, allows for a ``collapse" scenario. We obtain a general analytical expression for the transition probability $P_{α_1,α_2 \to 2}$ in going from an initial superposition state to the second eigenstate. Armed with this general expression we study some interesting special cases. With a delta-function pulse, the transitions are abrupt/instantaneous and we show that they do not depend on the energy gap $E_2-E_1$ and hence on the relative phase between the two eigenstates. For specific multiple values of the interaction strength $β$, we show that the system ends up in a definite eigenstate i.e. probability of unity. Such a transition can be viewed as a ``collapse" since a superposition of states transitions abruptly to a definite eigenstate. The collapse of the wavefunction is familiar in the context of a measurement. Here it occurs via a delta-function pulse in Schrödinger's equation. We discuss how this differs from a collapse due to a measurement.

We propose a protocol for implementing Barenco-type multi-qubit controlled gates using short driven spin chains. Starting from an Ising interaction with a transverse drive on the last spin, we construct an effective two-qubit Hamiltonian whose time evolution implements the Barenco gate $V_2(\varphi,ω,φ)$ and, in particular, a CNOT gate. We then embed this construction into a three-qubit $XXZ$ chain to realize the three-qubit Barenco gate $V_3(\varphi,ω,φ)$, which includes the Toffoli gate as a special case. The derivation is fully analytical: we perform a sequence of unitary transformations, identify decoupled subspaces, and apply a rotating-wave approximation to obtain simple effective Hamiltonians. We derive explicit conditions on the coupling strengths and driving parameters, provide closed-form expressions for the time-evolution operators in each relevant subspace, and characterize the quality of the implementation using the operator fidelity. Numerical simulations show that the protocol achieves high fidelities over broad parameter ranges, demonstrating its robustness and suitability for quantum information processing in spin-chain platforms.

The promise of unconditional security in the Quantum Key Distribution (QKD) depends on the availability of an authenticated classical channel. However, practical implementations often overlook this requirement or rely on computational assumptions that compromise long-term security. To overcome these challenges, this paper presents Entanglement-Based Authenticated Quantum Key Distribution (EAQKD), a novel protocol that addresses critical security and practical limitations in quantum cryptographic key exchange. Our approach integrates quantum entanglement distribution with information-theoretic authentication. We evaluate EAQKD's performance through a comprehensive discrete-event simulation framework modeled on realistic channel characteristics and experimental device parameters. Our modeling incorporates parameters from practical quantum optics setups, including SPDC entanglement sources, superconducting nanowire detectors, and fiber channel imperfections. Our results show quantum bit error rates consistently below the 11% security threshold (ranging from 1.86% at 10 km to 9.27% at 200 km), with secure key rates achieving $1.12 \times 10^5$ bits/s at short distances and maintaining practical rates of 9.8 bits/s at 200 km. When integrated with quantum repeater architectures, our analysis projects that EAQKD can extend secure communication beyond 500 km while providing information-theoretic security guarantees. Comparative analysis against the BB84, E91, and Twin-Field QKD protocols demonstrates EAQKD's superior balance of security, practical performance, and implementation robustness. This work advances quantum cryptography by providing a rigorously analyzed engineering reference for secure key distribution in future quantum communication networks.

Fredholm integral operators that commute with the Hamiltonians of certain quantum mechanical problems with quartic potentials are introduced. The operators are expressed in terms of an Airy function, and their eigenvalues fall off exponentially fast. They may help with high-accuracy numerical analysis, and their existence leads to dual descriptions in terms of infinite one-dimensional chains with variables on nodes, and weights on nodes and links. The systems discussed include the anharmonic quartic oscillator as well as multivariable potentials and higher dimensional systems, including certain quantum field theories with nonlocal interactions.

Entanglement asymmetry provides a quantitative measure of symmetry breaking in many-body quantum states. Focusing on inhomogeneous $U(1)$ charges, such as dipole and multipole moments, we show that the typical asymmetry is bounded by a specific fraction of its maximal value, and verify this behavior in several settings, including random matrix product states. Within the latter ensemble, by identifying the bond dimension with an effective time, we qualitatively reproduce recent findings on the entanglement asymmetry dynamics in random quantum circuits, thereby suggesting a universal dynamical structure of the asymmetry of $U(1)$ charges in local ergodic systems. Multipole charges naturally arise in systems with Hilbert-space fragmentation, where the dynamics splits into exponentially many disconnected sectors. Using the commutant algebra formalism, we generalize entanglement asymmetry to account for fragmentation. We derive general upper bounds for both conventional and fragmented symmetries and identify states that saturate them. While the asymmetry grows logarithmically for conventional symmetries, it can scale extensively in fragmented systems, providing a probe that distinguishes classical from genuinely quantum fragmentation.

There exist pairs of orthogonal Latin squares of any order n except if n=2 or n=6 [Bose, Shrikhande and Parker, 1960]. In particular, the problem of Euler's thirty-six officers does not have a solution. However, it has a "quantum solution": there exist so-called entangled quantum Latin squares of order six [Rather et al., 2022]. We prove that mutually orthogonal quantum Latin squares of order six do not exist if entanglement is not allowed.

Single photons enable the distribution of quantum information over large distances and thus play a major role in quantum technologies such as communication and computing. Solid-state emitters are practical and efficient sources of single photons that can be manufactured in large numbers. When combined with a spin, the resulting spin-photon interfaces can store quantum states for extended periods and serve as the basis for quantum networks and repeaters. Among the many host materials explored over the past few decades, silicon stands out for its advanced nanofabrication, the maturity of its integrated photonics and microelectronics, and its high isotopic purity, which leads to exceptionally long spin coherence. These properties position silicon single-photon emitters and spin-photon interfaces among the most promising hardware platforms for implementing quantum networks and distributed quantum information processors. This review summarizes the current state of the art and open challenges towards coherent single-photon sources and scalable spin-photon interfaces based on color centers and erbium dopants in nanophotonic silicon structures.

We introduce a novel quantum algorithm for the lattice Boltzmann method (LBM) based on the one-step simplified LBM. The structure of the algorithm allows for more flexibility in modelling different physics in contrast to earlier quantum algorithms for the LBM, while retaining computational efficiency in terms of the gate and qubit complexity. The new algorithm has potential for full end-to-end quantum utility especially for linear problems. We discuss the implementation of examples in linear acoustics, as well as a nonlinear Navier-Stokes problem that was solved on an IBM QPU in a hybrid simulation loop.

Shared randomness is the central ingredient for stabilizing symmetrizable communication systems against arbitrarily varying jammers. Given the presence of the jammer, however, the question arises how this precious resource could have been distributed. Several works discuss the use of external sources for this task. In this work, we show, based on the most standard optical communication model, how the sender and receiver can employ either classically correlated thermal light or entangled two-mode squeezed states created at and transmitted by the sender to counter the jamming attack of an energy-limited jammer during the distribution phase. Both sender and receiver are only allowed to use homodyne detection in our model, and the sender has to obey a power limit as well.

Levitated ferromagnetic systems are expected to have significant potential in precision magnetic field sensing by leveraging mechanical isolation to minimize mechanical contact and associated noise. Here, we report the implementation of a high-sensitivity magnetometer based on a levitated ferromagnetic torsion oscillator, incorporating a centroid tracking method for superior measurement resolution and noises reduction. The device, featuring a compact sensor volume of $(2.5 \, \rm{mm})^3$ and operating under room temperature, attains a remarkable magnetic sensitivity of {$391\pm 59 \, \rm{fT\cdot Hz^{-1/2}}$}. This capability enables precise detection of weak magnetic fields and provides a novel platform for exploring exotic interactions beyond the Standard Model. These results demonstrate that the levitated torsion oscillator system not only serves as a powerful tool for high-precision magnetic sensing but also holds promise for advancing breakthroughs in fundamental physics.

Excitons in anisotropic two-dimensional (2D) materials, defined by direction-dependent effective masses, are of pronounced interest for their roles in excitonic and magneto-optical phenomena. A perpendicular magnetic field complicates the separation of center-of-mass (c.m.) and relative motions, especially when electron and hole masses are comparable. Conventional theories often employ an approximate c.m. separation using factorized wave functions, modifying magnetic Hamiltonian terms and possibly introducing inaccuracies in magnetoexciton energy predictions. This work develops an exact analytical framework for c.m. and relative motion separation in anisotropic 2D magnetoexcitons, without resorting to the stationary-c.m. approximation. Starting from the full electron-hole Hamiltonian in a homogeneous magnetic field, the formalism uses the conserved pseudomomentum to derive a relative-motion Hamiltonian, revealing new anisotropy-dependent couplings and magnetic coefficients absent in approximate models. The resulting Schrödinger equation is treated via the Feranchuk-Komarov operator method and Levi-Civita transformation, allowing non-perturbative, systematically convergent solutions. Application to monolayer black phosphorus and titanium trisulfide, both freestanding and encapsulated in hexagonal boron nitride, yields magnetoexciton energies, diamagnetic coefficients, and probability densities for the ten lowest states across considerable magnetic-field ranges. The results demonstrate the significant influence of anisotropy-dependent coupling on magnetic response in systems with strong mass anisotropy. This formalism is generalizable to other anisotropic 2D semiconductors, establishing a foundation for advanced magneto-optical studies.

We propose to use the antisqueezing-enhanced non-Gaussian Schrödinger cat quantum states of the probing light for the task of detection of a given phase shift in optical interferometers. We show that the antisqueezing allows to increase the robustness of the setup to optical losses. We find the optimal degrees of the antisqueezing for experimentally achievable values of the Schrödinger cat amplitude and the optical losses and compare the resulting sensitivity with the one provided by the Gaussian squeezed states.

The intersection between quantum mechanics and gravitational physics has been providing challenging puzzles for decades. In this thesis, we study the dynamics of an open quantum system coupled with a bath of gravitons, the quanta of the gravitational field in the linear limit of general relativity. We focus on two main aspects. First, we analyze the decoherence induced by gravitons when we consider the open system to be described by both external and internal degrees of freedom. Since gravity is universal, the internal variables also interact with the gravitons, and here we show that this interaction leads to the decoherence of spatial superpositions of microscopic systems in the long-time regime, even when the graviton bath alone does not. We then proceed to the second main aspect, which is the entropy production that arises when an external agent drives a quantum system through the graviton bath. This irreversibility comes from quantum fluctuations of spacetime itself and, as such, has a fundamentally universal aspect.

Computing excitonic spectra in quasicrystal and super-moiré systems constitutes a formidable challenge due to the exceptional size of the excitonic Hilbert space. Here, we demonstrate a tensor-network method for the real-space Bethe-Salpeter Hamiltonian, allowing us to access the spectra of an excitonic $10^{18}$-dimensional Hamiltonian, and enabling the direct computation of bound-exciton spectral functions for systems exceeding one billion lattice sites, several orders of magnitude beyond the capabilities of conventional approaches. Our method combines a tensor-network encoding of the real-space Bethe-Salpeter Hamiltonian with a Chebyshev tensor network algorithm. This strategy bypasses explicit storage of the Hamiltonian while preserving full real-space resolution across widely different length scales. We demonstrate our methodology for one- and two-dimensional super-moiré systems, achieving the simultaneous resolution of atomistic and mesoscopic structures in the excitonic spectra in billion-size systems, showing exciton miniband formation and moiré-induced spatial confinement. Our results establish a real-space methodology enabling the simulation of excitonic physics in large-scale quasicrystal and super-moiré quantum matter.

Their exceptional coherence makes nuclear spins in solids a prime candidate for quantum memories in quantum networks and repeaters. Still, the direct all-optical initialization, coherent control, and readout of individual nuclear spin qubits have been an outstanding challenge. Here, this is achieved by embedding 167-Er dopants in yttrium orthosilicate in a cryogenic Fabry-Perot cavity, whose linewidth of 65 MHz is much smaller than the 0.9 GHz separation of neighboring hyperfine levels. Frequency-selective emission enhancement thus enables a single-shot readout fidelity of 91(2)%. Furthermore, a large magnetic field freezes paramagnetic impurities, leading to coherence times exceeding 0.2 s. The combination of nuclear-spin qubits with frequency-multiplexed addressing and lifetime-limited photon emission in the minimal-loss telecommunications C-band establishes 167-Er as a leading platform for long-range, fiber-based quantum networks.

Quantum networks are advancing the information technology infrastructure of society. Simulation and emulation software tools have emerged to support the design, development, and deployment of quantum networks, however, classical simulation and emulation methods have major bottlenecks in the error, latency, and cost that they can achieve at scale. In this work, we review quantum network simulation and emulation tools, including foundational principles, state-of-the-art tools, and bottlenecks. We then discuss how quantum technologies can address these challenges, and we construct a roadmap for the adoption of quantum simulation and emulation tools, emphasizing codesign with quantum network testbeds.

Determining the work statistics of quantum engines is challenging due to measurement backaction. We here show that a dynamic Bayesian network-based measurement scheme, which preserves quantum coherence within an engine cycle, is minimally invasive, in the sense that the averaged measured state over one cycle exactly coincides with the unmeasured state. It therefore provides a general framework to investigate energy exchange statistics in quantum machines. This stands in contrast to the standard two-point measurement protocol, whose backaction can be so strong that it generally fails to reproduce the average work output of a coherent motor. It may even alter its mode of operation, causing it to cease functioning as an engine under observation. We further demonstrate that recently proposed universal fluctuation bounds do not necessarily apply to coherent machines.

Geometric phases play a fundamental role in understanding quantum topology, yet extending the Uhlmann phase to non-Hermitian systems poses significant challenges due to parameter-dependent inner product structures. In this work, we develop a comprehensive theory of the Uhlmann phase for quasi-Hermitian systems, where the physical Hilbert space metric varies with external parameters. By constructing a generalized purification that respects the quasi-Hermitian inner product, we derive the corresponding parallel transport condition and Uhlmann connection. Our analysis reveals that the dynamic metric induces emergent geometric features absent in the standard Hermitian theory. Applying this formalism to solvable two-level models, we uncover rich finite-temperature topological phase diagrams, including multiple transitions between trivial and nontrivial phases driven by thermal fluctuations. Crucially, the quasi-Hermitian parameters are shown to profoundly influence the stability of topological regimes against temperature, enabling nontrivial phases to persist within finite-temperature windows. Furthermore, by extending established interferometric protocols originally developed for Hermitian systems, the geometric amplitude can be recast as a measurable Loschmidt fidelity between purified states, providing a practical and experimentally accessible pathway to investigate quasi-Hermitian mixed-state geometric phases and their finite-temperature transitions. This work establishes a unified framework for understanding mixed-state geometric phases in non-Hermitian quantum systems and opens a practical avenue for their experimental investigation.

Tin-phthalocyanine molecules adsorbed on a NaCl ultrathin film on Au(111) exhibit electrofluorescence excited by a current across a scanning tunneling microscope junction. Exploring the dependence of the molecular monomer photon yield on the injected current evidences the one-electron excitation process underlying the neutral-exciton luminescence. Photon spectra of the monomer exhibit vibrational progression and hot luminescence, while the dimer electrofluorescence spectroscopic fine structure results from the coupling of the adjacent optical transition dipoles. The photon yield of the dimer is significantly altered upon changing the configurational state of one of the two molecules. In one of the bistable configurations light emission is amplified compared to the monomer, and it is reduced in the other.

We discuss fundamental features of the local expression for the entropy production rate of the nonequilibrium quantum dynamics of open systems and its relations to memory effects and the spectrum of the generator of the dynamics. Defining the entropy production rate as negative rate of change of the relative entropy with respect to an instantaneous fixed point, it is shown that positivity of the entropy production rate for all possible initial states implies that the real parts of the eigenvalues of the time-local generator for the quantum master equation are always negative. It is further demonstrated that Markovian dynamics, identified as P-divisibility of the quantum dynamical map, implies positivity of entropy production rate, thus providing a kind of generalized second law in the nonequilibrium regime. We also prove by means of the counterexample of a phase covariant quantum master equation that the converse of this statement is not true, i.e., there are non-Markovian dynamics for which the entropy production rate is always positive. Thus, we conclude that the emergence of negative entropy production rates is a sufficient but not necessary condition for non-Markovianity of the quantum dynamics. Finally, we also consider a recently introduced map-based notion of entropy production and show the equivalence between its positivity and Markovianity for general finite-dimensional systems.

Many properties of a quantum system can be obtained from just a single eigenstate of its Hamiltonian. For example, a single eigenstate can be used to determine whether a system is integrable or chaotic and, in the latter case, to establish its thermal properties. Focusing on the XXZ model, we show that the local integrals of motion, which lie at the heart of integrability, can also be estimated from a small number of eigenstates. Moreover, as the system size increases, fewer eigenstates are required, so that in the thermodynamic limit, the integrals of motion can be obtained from a vanishingly small fraction of all eigenstates. Interestingly, this property does not extend to integrals of motion arising solely from Hilbert space fragmentation, as found in the folded XXZ model, where the majority of eigenstates has to be used. This represents one of the few fundamental differences known between integrability and Hilbert space fragmentation.

As silicon spin qubit chips are increasing in qubit number and area, methods for the screening of qubit related material parameters become vital. Here we demonstrate the two-dimensional mapping of small variations of the electron g-factor of quantum dots formed in planar Si/SiGe quantum wells with precision better than $10^{-3}$ and with nanometer lateral resolution. We scan the electron g-factor across a 40 nm $\times$ 400 nm area and observe two g-factors per QD site which obey a striking symmetry and bimodal distribution across the area. These two g-factors relate to valley states of the electron in the quantum dot in agreement with a recent theoretical model. Using conveyor-belt shuttling of entangled electron spin pairs, complementary to the mapping of the local valley-splitting, we map the g-factor. We compare g-factor and valley splitting maps measured on the same device, and extract the complex intervalley coupling parameter along the shuttle trajectories applying a theoretical model of g-factor dependence on intervalley coupling. These maps will allow unprecedented insights into the spin-valley dynamics during qubit manipulation, readout and shuttling and serve as a benchmark for the engineering of Si/SiGe heterostructures for large-scale quantum chips.

We show how the dynamics of a specific subset of states can be separated from the dynamic of the total quantum state via a time-dependent projector-based formalism of adiabatic elimination. Within our formalism, we assume explicit time dependency in the coupling between both subsystems. Additionally, we do not assume that the elements of the Hamiltonian commute, as in matter-wave optics this not given in general. Here the center-of-mass degrees of freedom frequently need to be taken into account. Our formalism allows to perform the adiabatic elimination in such a setting.

Superpositions of mass distributions can potentially lead to entanglement with the geometry of spacetime. Here we show that there exists a natural reluctance for macroscopic mass distributions to form such superpositions. The macroscopic superposition is modeled as a Schr{ö}dinger cat state. The reluctance manifests as a dip in the total energy of the Schr{ö}dinger cat state as a function of the separation distance between the terms in the superposition. The dip in the energy provides an opposing force preventing the formation of the superposition. A generalization of this phenomenon addressing the measurement problem is also discussed.

We study finite-layer alternations of the \emph{Constraint--Enhanced Quantum Approximate Optimization Algorithm} (CE--QAOA), a constraint-aware ansatz that operates natively on block one-hot manifolds. Our focus is on feasibility and optimality guarantees. We show that restricting cost angles to a harmonic lattice exposes a positive Fejér filter acting on the cost-phase unitary $U_C(γ)=e^{-iγH_C}$ \emph{in a cost-dephased reference model (used only for analysis)}. Under a wrapped phase-separation condition, this yields \emph{dimension-free} finite-depth and finite-shot lower bounds on the success probability of sampling an optimal solution. In particular, we obtain a ratio-form guarantee \[ q_0 \;\ge\; \frac{x}{1+x}, \qquad x \;=\; (p{+}1)^2 \sin^2(δ/2)\,C_β, \] where $q_0$ is the single-shot success probability, $C_β$ is the mixer-envelope mass on the optimal set, $δ$ is a phase-gap proxy, and $p$ is the number of layers. Riemann--Lebesgue averaging extends the discussion beyond exact lattice normalization. We conclude by outlining coherent realizations of hardware-efficient positive spectral filters as a main open direction.

Generalized quantum measurements play a crucial role in quantum mechanics, and symmetric informationally complete positive operator-valued measurements (SIC POVMs) provide a powerful and flexible framework for extracting information from quantum systems. However, the existence of SIC-POVMs in every finite dimension remains an open question, which has stimulated extensive research into alternative classes of POVMs. Recently, Geng $et$ $al$. [Phys. Rev. Lett. 126, 100401 (2021)] proposed a broader class of SIC POVM, called semisymmetric informationally complete POVM (semi-SIC POVM), which extends beyond SIC POVM. In this work, we focus on the four-outcome POVMs and experimentally realize the semi-SIC POVMs using a one-dimensional discrete-time quantum walk. Additionally, employing single photons and linear optics, we perform an experimental self-testing of semi-SIC POVMs in the semi-device-independent manner. Our results pave the way for exploring quantum certification with generalized quantum measurements.

Thermal light, including blackbody radiation and spontaneous emission, exhibits photon bunching. Thermal light sources, however, typically yield low spectral densities, limiting their practical utility. Pseudothermal light sources with higher brightness and longer coherence time are often employed instead. While pseudothermal light also exhibits photon bunching, this property may not suffice to fully replicate the behavior of genuine thermal light. Here we demonstrate a method to directly test the Siegert relation for two sources of photon-bunched light, laser light scattered from a rotating ground glass and spontaneously emitted light from a gas discharge lamp, probing a fundamental criterion expected of thermal light.

Quantum frequency conversion (QFC) is essential for bridging the spectral gap between stationary qubits and low-loss optical communication channels. In this work, we demonstrate a short-wavelength-pumping QFC with the first-order quasi-phase matching period of 3.07 um on thin-film lithium niobate, converting ultraviolet photons to the telecom C-band. By constructing a theoretical model that correlates the normalized conversion efficiency with domain defects in the short-period phase-matched waveguide, we found the critical tolerance of domain defects along the waveguide should be $\le 2$ (excluding the ends). Based on this, we achieved a theoretical limit normalized conversion efficiency of 839%/(W*cm^2) for the fundamental guided mode through fabrication optimization. Furthermore, we propose a robust noise suppression strategy for short-wavelength pumping by utilizing the counter-tuning behaviors of difference-frequency generation and spontaneous parametric down-conversion. By combining these advances with ultra-narrowband filtering, we achieve a record-high external efficiency of 28.8% and an ultra-low noise of 35 counts per second. This high-performance QFC connecting ultraviolet and telecom bands satisfies the stringent requirements for long-lived remote ion-ion entanglement in scalable quantum networks [W.-Z. Liu et al., Nature (2026)].

We demonstrate the novel effect of gain-induced spectral shifts in the type-II parametric down-conversion (PDC) process, which results in a transition from degenerate to non-degenerate PDC with increasing parametric gain. This effect, originating from the second-order dispersion terms, significantly alters the properties of PDC in the high-gain regime, where it leads to increased distinguishability of the generated photon pairs. The effect is established by evaluating a rigorous theoretical model, which is based on solving a system of coupled integro-differential equations for monochromatic operators. The widely used spatially-averaged approximate model fails to reproduce this important effect.

The exchange of quantum information among nodes in a quantum network is one of the main challenges in modern technologies. Superconducting waveguide QED networks hold great potential for realizing distributed quantum computation, where distinct nodes communicate via itinerant single photons. Yet, different frequencies among the nodes restrict their applicability and limit scalability. Here we derive the controls required to shape single photons arbitrarily detuned with respect to their natural frequency, allowing thus for an on-demand and deterministic exchange of quantum information among frequency detuned nodes. We provide a theoretical framework, analyzing the properties of the controls for typical photon shapes, identifying operation regimes amenable for experimental realization. We then show how these controls enable frequency-selective quantum state transfer among non-resonant and distant nodes of a realistic network. In addition, we also provide a simple extension for remote entanglement generation between these nodes. The suitability and high-fidelity of these protocols is supported by numerical simulations, highlighting the novel networking possibilities unlocked when shaping frequency-tunable single photons.

The quantum phonon laser state is a vibrational state generated by phonon coherent amplification based on quantum mechanics. Its core is coherent excitation and manipulation of phonon quantum states by controlling phonon dynamics. This technology breaks classical limits of traditional phonon lasers, offering new methods for quantum information. Previous research on quantum phonon lasers focused on quantum van der Pol oscillators. As typical nonlinear quantum systems, they show significant value in trapped-ion systems. These breakthroughs extend nonlinear dynamics into the quantum domain and provide platforms for exploring quantum nonlinear phenomena. Although realized in two-ion systems, practical applications remain challenging. This paper explores how a single trapped ion generates quantum phonon laser states using a three-level model. By solving the quantum master equation numerically, steady-state characteristics are analyzed, focusing on quantum statistics including the Wigner function and second-order correlation function. An experimental scheme is proposed based on a single trapped 40Ca+ ion, using bichromatic blue-sideband and red-sideband lasers to generate quantum phonon laser states. By introducing the characteristic function of motional states, precise quantum state tomography is achieved. Additionally, a two-level model discusses the phonon laser threshold effect. However, the three-level model shows significantly different thresholds and more accurately describes the quantum phonon laser's physical mechanisms.

We identify an operational principle that singles out Projection-Valued Measures (PVMs) among general Positive Operator-Valued Measures (POVMs), bridging the modern quantum measurement theory and the traditional formulation based on projective measurements of physical observables. We reformulate Ozawa's intersubjectivity condition, which requires inter-observer agreement of the measurement outcomes, in a quantitative manner within the framework of generalized probabilistic theories. We prove that (i) a POVM is a PVM if and only if its every coarse-graining is intersubjective, and (ii) a system is classical if and only if intersubjectivity is preserved under any coarse-graining, establishing a complete characterization of the physical observables and the classical theory. Furthermore, measurements with intersubjectivity are sufficiently rich for the informational tasks of state tomography and state discrimination, testifying to its operational significance in quantum and beyond information processing.

We investigate how internal coupling influences the operation and performance of a quantum Otto cycle operating as the Gibbs-state limit cycle (GSLC), equilibrating limit cycle (ELC), and non-equilibrating limit cycle (NELC). We show that the internal coupling significantly broadens the operational regime of the cycle. In particular, in parameter regimes where the uncoupled Otto cycle fails to operate as any thermal machine, the coupled system can function as an engine or a refrigerator. For the GSLC, in which we assume that the system quickly equilibrates during the isochoric processes, the internal coupling not only shifts and enlarges the operational regime but also enhances the efficiency and the coefficient of performance (COP), allowing the performance to exceed the standard Otto bounds while remaining below the Carnot limit. For ELC and NELC, we validate the global approach of the Gorini--Kossakowski--Sudarshan--Lindblad (GKSL) master equation by comparison with the GSLC, and examine the NELC for finite interaction time and the ELC for infinite interaction time. Although the efficiency and COP of NELC are lower than those of ELC, shorter interaction times yield higher power output, consistent with the power--efficiency trade-off.

We develop a general theory of Landau-Zener (LZ) tunneling in a two-level system with amplitude-dependent, sign-reversible nonlinear coupling, distinguishing it fundamentally from conventional on-site nonlinearity. Through a combination of analytical and phase-space analysis, we show that beyond a critical interaction strength, the nonlinear coupling fundamentally reshapes the adiabatic energy landscape, introducing a topological twisted and knotted structure. This structure leads to a complete breakdown of the standard exponential LZ formula, even in the adiabatic limit. Central to this anomalous behavior is the emergence of a black-hole-like fixed point, which acts as a universal attractor: upon traversing the critical region, all quantum trajectories converge to this fixed point, irreversibly erasing any memory of the initial state. From this fixed-point picture, we derive an exact analytical expression for the adiabatic tunneling probability, revealing a characteristic power-law dependence on both linear and nonlinear coupling strength. Our work establishes a paradigmatic framework for nonlinear-coupling-induced anomalous adiabaticity breaking and offers a universal mechanism for state control in driven quantum and wave systems.

Efficiently characterizing large quantum states and processes is a central yet notoriously challenging task in quantum information science, as conventional tomography methods typically require resources that grow exponentially with system size. Here, we introduce a provably efficient and structure-agnostic learning framework for noisy $n$-qubit quantum circuits under generic noise with arbitrary noise strength. We first develop a sample-efficient learning algorithm for unital noisy quantum states. Building on this result, we extend the framework to quantum process tomography, obtaining a unified protocol applicable to both unital and non-unital channels. The resulting approach is input-agnostic and does not rely on assumptions about specific input distributions. Our theoretical analysis shows that both state and process learning require only polynomially many samples and polynomial classical post-processing in the number of qubits, while achieving near-unit success probability over ensembles generated by local random circuits. Numerical simulations of two-dimensional Hamiltonian dynamics further demonstrate the accuracy and robustness of the approach, including for structured circuits beyond the random-circuit setting assumed in the theoretical analysis. These results provide a scalable and practically relevant route toward characterizing large-scale noisy quantum devices, addressing a key bottleneck in the development of quantum technologies.

Anderson localization is a fundamental phenomenon in disordered quantum systems, where transport is suppressed by wave interference from extensive randomness. Moving beyond traditional multi-impurity scenarios, we investigate impurity-induced localization phenomena in low-dimensional tight-binding systems by focusing on the properties of impurity-generated bound states. By introducing a single on-site impurity into an otherwise extended lattice, we demonstrate that the impurity can host a bound state whose spatial character undergoes a transition from extended to localized as the impurity strength surpasses a critical value. This transition pertains solely to the impurity state, while the bulk states of the host system remain extended. We characterize the localization behavior by analyzing two distinct spatial profiles of the bound states: one with symmetric decay and another with exponential decay from the impurity site. Our results highlight how a local perturbation can induce nontrivial localization behavior at the level of individual eigenstates, without implying a global localization transition of the underlying electronic system.

Differently from the non-relativistic quantum mechanics, the violation of Bell inequalities in quantum field theory depends more on the structure of observable algebras (typically type III von Neumann algebras) rather than the choice of specific quantum states. Therefore, studying the violation of Bell inequalities based on the von Neumann algebraic framework often reveals information about the algebraic structure. In this paper, we employ three mutually-commuting von Neumann algebras to characterize quantum entanglement swapping networks, and establish Bell-like inequalities thereon, commonly referred to as bilocal inequalities. We investigate the algebraic structural conditions under which bilocal inequalities are satisfied or violated on the generated algebra of these three von Neumann algebras. Furthermore, the conditions for maximal violation of the inequalities can be utilized to infer the structural information of von Neumann algebras in reverse. Our results not only utilize the violation of bilocal inequalities to reveal the structural properties of von Neumann algebras, but can also be applied to quantum mechanics and quantum field theory.

Grover's algorithm provides a quadratic speedup over classical algorithms for searching unstructured databases and is known to be strictly optimal in oracle query complexity, with tight bounds on its success probability. Although the standard Grover search cannot be further accelerated in the full-search setting, a trade-off between accuracy and query complexity gives rise to the partial search problem. The Grover-Radhakrishnan-Korepin (GRK) algorithm is widely regarded as the optimal protocol for this task. In this work, we provide strong evidence for the strict optimality of the GRK operator sequence among all admissible compositions of global and local Grover operators. By exhaustively examining all operator sequences with a fixed number of oracle queries, we show that the GRK structure universally maximizes the success probability. Building on this result, we derive an asymptotically tight upper bound on the maximal success probability for partial search and establish a matching lower bound on the minimal expected number of oracle queries. Furthermore, we investigate parallel quantum search within the partial-search framework. While a direct GRK-based parallelization does not outperform established parallel Grover schemes, we demonstrate that a hybrid strategy combining partial and full search protocols achieves a strictly improved parallel efficiency. Our results clarify the fundamental limits of quantum partial search and its role in optimizing parallel quantum search algorithms.

The generalized quantum master equation provides a powerful framework for non-Markovian dynamics of open quantum systems. However, the accurate and efficient evaluation of the memory kernel remains a challenge. In this work, we introduce a comprehensive tensorial extension to the Memory Kernel Coupling Theory (MKCT) to overcome this bottleneck. By elevating the original scalar formalism to a tensorial framework, the extended MKCT enables the calculation of general expectation values and cross-correlation functions. We demonstrate the numerical accuracy and efficiency of this method across multiple benchmark systems: capturing transient populations and coherences in the spin-boson model, resolving the excitonic absorption spectrum of the Fenna-Matthews-Olson complex, and simulating charge mobility in one-dimensional lattice models. These successful applications establish the tensorial MKCT as a highly efficient tool for investigating complex dynamics in open quantum systems.

Circuit cutting partitions a large quantum circuit into smaller subcircuits that can be executed independently and recombined by classical post-processing. In classical state-vector simulation with full-state reconstruction, the runtime is governed by a trade-off between reduced subcircuit size and the overheads of exponentially many subcircuits and full-state reconstruction. For equal partitioning, we derive threshold conditions on the number of cuts below which cutting reduces the wall-clock time. State-vector experiments validate the predicted speedup boundary up to 24 qubits, and a runtime breakdown up to 30 qubits identifies crossovers at $q \approx 18$ and $q \approx 22$ where merging overtakes first preprocessing and then subcircuit simulation. As a practical guideline, we show that under a 10-minute wall-clock budget, two-way cutting extends the maximum feasible qubit count by 4 to 6 qubits relative to simulation without cutting.

We study nonreciprocal bipartite and tripartite entanglement in a spinning exciton-optomechanical system (EOMS) with an optical parametric amplifier (OPA). We demonstrate that nonreciprocal entanglement among photons, excitons, and phonons can be achieved under experimentally feasible parameters. We find that the nonreciprocal entanglement induced by Sagnac effects can be regulated through the OPA. Particularly, We show that the OPA significantly enhances photon-exciton entanglement and tripartite entanglement but weakens photon-phonon and exciton-phonon entanglement. Moreover, we find that the photon-exciton nonreciprocal entanglement not only can be generated at room temperature and even higher temperature but also exhibits highly robustness to cavity dissipation. Our works open a way to manipulate the room-temperature nonreciprocal entanglement, which may be useful for developing nonreciprocal quantum technologies.

We study a quantum-algorithmic framework for parameterizing partial differential equations (PDEs). For a broad class of problems in which the discretized parameter field admits a diagonal representation, block-encodings of diagonal matrices, or diagonal block-encodings, can be used to represent spatially varying coefficients with structured, potentially complicated profiles. This encoding enables efficient quantum simulation of forward PDEs and extends naturally to parameter-dependent settings. Such simulations are a key primitive for quantum algorithms for PDE-constrained optimization, where the goal is to identify optimal design parameters. We illustrate the framework numerically through forward simulation and parameter design for the two-dimensional wave equation with a Gaussian parameter profile.

In this paper, we present a technically simple method to establish upper bounds on the expected injective norm of real and complex random tensors. Our approach is somewhat analogous to the moment method in random matrix theory, and is based on a deterministic upper bound on the injective norm of a tensor which might be of independent interest. Compared to previous approaches to these problems (spin-glass methods, epsilon-net techniques, Sudakov-Fernique arguments, and PAC-Bayesian proofs), our method has the benefit of being nonasymptotic, relatively elementary, and applicable to non-Gaussian models. We illustrate our approach on various models of random tensors, recovering some previously known (and conjecturally tight) bounds with simpler arguments, and presenting new bounds, some of which are provably tight. From the perspective of statistical physics, our results yield rigorous estimates on the ground-state energy of real and complex, possibly non-Gaussian, spin glass models. From the perspective of quantum information, they establish bounds on the geometric entanglement of random bosonic states and of random states with bounded multipartite Schmidt rank, both in the thermodynamic limits as well as the regimes of large local dimensions.

High-dimensional quantum systems offer a new playground for quantum information applications due to their remarkable advantages such as higher capacity and noise resistance. We propose potentially practical schemes for remotely preparing four- and eight-level equatorial states in complex Hilbert spaces exactly by identifying a set of orthogonal measurement bases. In these minimal-resource-consuming schemes, both pre-shared maximally and non-maximally entangled states are taken into account. The three-, five-, six-, and seven-level equatorial states in complex Hilbert spaces can also be obtained by adjusting the parameters of the desired states. The evaluations indicate that our high-dimensional RSP schemes might be possible with current technology. The collection operations, necessary for our high-dimensional RSP schemes via partially entangled channels, can be avoided by encoding the computational basis in the spatial modes of single-photon systems.

Preparation of nonclassical light with special quantum properties is essential for quantum technologies. High-harmonic generation (HHG) is a process which not only enables the creation of attosecond pulses but also has the potential to generate light with intricate quantum properties. In a recent experiment [1], nonclassical inter-harmonic correlations have been measured from a HHG source. In this work, we theoretically investigate entanglement between different harmonics within an effective quantum optical model. This model implements a signifcant degree of simplifcation regarding the processes within the target material, treating the material through susceptibilities, as it is usual in quantum optics. Such an approach yields a general description of HHG, permitting the implications that can be derived within it to hold broadly. We find that entanglement is produced as a result of the often neglected back-action. We can qualitatively reproduce experimentally measured nonclassicalities, which suggests that intermodal entanglement can, to an extent, be considered a universal phenomenon associated with HHG, rather than a result of using specific material targets.

We study the prepare-and-measure scenario in which Alice transmits a quantum system to Bob, who then performs a quantum measurement. The quantum state of the system is unknown to Bob, and the measurement is unknown to Alice. It has recently been shown that shared randomness and two bits of classical communication are necessary and sufficient to simulate the transmission of a qubit. We show that the communication cost can be reduced to an average of $1.89$ bits. We then study restricted sets of state preparations: First, for a restriction to real-valued qubit states, if the communication of a classical trit is sufficient, we show that the corresponding protocol must have a convoluted form. We then reduce the smallest qubit scenario requiring two bits of classical communication to only $6$ state preparations and $5$ measurements. For a qutrit, it is not known whether the communication cost is finite; we identify a scenario that requires at least $5$ classical messages, already for the simulation of the real qutrit. Finally, we develop a method for restricted sets of states, that allows us to lower bound the classical communication cost based solely on the set of quantum states.

We show that a simple qubit-regularized $\mathrm{SU}(3)$ lattice gauge theory (LGT) on a plaquette chain admits a continuum limit with massive glueball excitations, providing a minimal toy model of strong interactions without quarks. By mapping the plaquette-chain Hamiltonian to the three-state quantum clock model in a magnetic field, we demonstrate that the theory can be tuned to a continuum limit governed at short distances by the $\mathbb{Z}_3$ parafermion conformal field theory (CFT), which serves as the ultraviolet (UV) fixed point. A small relevant magnetic perturbation then drives the system to a massive continuum quantum field theory in the infrared (IR). The resulting relativistic massive particles can be interpreted as quasi one-dimensional analogues of glueballs. In the continuum theory we compute the ratio of the lowest glueball masses with opposite charge conjugation to be $m^{-}/m^{+} = \,1.459(2)$ and find $\sqrtσ/m^{+}\,= 0.2648(2)$, where $σ$ is the string tension between a static quark and antiquark.

Quantum networks are envisioned to enable reliable distribution and manipulation of quantum information across distances, forming the foundation of a future quantum internet. The fair and efficient allocation of communication resources in such networks has been addressed through the quantum network utility maximization (QNUM) framework, which optimizes network utility under the assumption of predetermined routes for competing user demands. In this work, we relax this assumption and aim to identify optimal routes that correspond to the maximum achievable network utility. Specifically, we formulate the single-path utility-based entanglement routing problem as a Mixed-Integer Convex Program (MICP). The formulation is exact when negativity is chosen as the entanglement measure for utility quantification or the network supports sufficiently high entanglement generation rates across demands. For other entanglement measures considered, the formulation approximates the problem with over 99.99% accuracy on evaluated real-world examples. To improve computational tractability, we propose a randomized rounding-based heuristic and an upper bound via the relaxation of the MICP. Furthermore, based on min-congestion routing, we introduce an alternative randomized heuristic and upper bound. This heuristic is computationally faster, while both the heuristic and the upper bound often outperform their counterparts on considered real-world networks. Our work provides the framework for extending classical flow-based and quality of service-aware routing concepts to quantum networks.

Single photons are the flying qubits of choice for distributing entanglement in a quantum internet. Quantum memories embedded in quantum repeaters are crucial to overcome transmission loss and enhance the rate of quantum communication. A multimode memory can further boost the channel capacity. However, benchmarking and building a practical quantum memory that simultaneously optimizes multiple performance metrics poses two key challenges. Here, we introduce quantum interconnect rate to comprehensively quantify quantum memories, and further demonstrate a high-performance quantum memory that simultaneously integrates three essential criteria at once: large multimode capacity, high efficiency, and high fidelity. Operating on 11-dimensional spatial modes, our memory achieves a uniform efficiency exceeding 80% and qubit storage fidelities above 99%, enabling the efficient storage of high-dimensional qudits. Based on these capabilities, we estimate a distribution of 3.56 bits of quantum information over a 1000-km repeater link in one minute, highlighting a practical pathway toward scalable quantum interconnects and quantum networks.

We analyze the geometric phase and dynamic phase acquired by a qubit coupled to an environment through pure dephasing, establishing a direct connection between phase accumulation and ergotropy. We show that the dynamic phase depends solely on the incoherent ergotropy, reflecting its purely energetic origin. In contrast, the geometric phase exhibits a nontrivial dependence on both the coherent and incoherent contributions to the total ergotropy, encoding the interplay between coherence, dissipation, and energy extraction. By performing a perturbative expansion in the qubit-environment coupling strength, we demonstrate that, in the weak-coupling and long-time regime, the geometric phase becomes determined exclusively by the incoherent ergotropy, which coincides with the asymptotic value of the total ergotropy reached under decoherence. These results provide a clear physical distinction between dynamic and geometric phases in open quantum systems and establish geometric phases as sensitive probes of energetic resources. Furthermore,~in superconducting circuit implementations, our findings suggest that the ergotropy of a two-level system could be inferred indirectly from geometric-phase measurements using standard techniques such as quantum state tomography.

This paper studies multipartite observables formed from sums of local self-adjoint contractions on tensor product Hilbert spaces. The square of such a sum has a parity structure: after decomposing each local product into commutator and anticommutator parts, the odd parity terms cancel and only even parity contributions remain. This yields a norm bound in terms of a family of pairwise defect weights built from local commutator and anticommutator norms. These defect weights also control an information theoretic estimate. The excess of the observable expectation above the product state threshold is shown to necessarily carry a definite amount of total correlation. Under a natural $\ell^{2}$-type bound on each local family, this product state threshold becomes explicit, which leads to a fully explicit lower bound on total correlation. A simple depolarizing example illustrates the resulting decay mechanism under local noise.