Quantum Computing Paradigms: Boson Sampling QC (Gaussian & Non-Gaussian)

(For other quantum computing paradigms and architectures, see Taxonomy of Quantum Computing: Paradigms & Architectures)

What It Is

Boson Sampling is a specialized, non-universal model of quantum computation where the goal is to sample from the output distribution of indistinguishable bosons (typically photons) that have passed through a passive linear interferometer​. In simpler terms, one prepares multiple photons, sends them through a network of beam splitters and phase shifters (a linear optical circuit), and then measures how many photons exit in each output mode. The resulting pattern of detection (which output ports registered photons) is a sample from a complicated probability distribution. This distribution is determined by the quantum interference of all the ways bosons can scatter through the network. The task may sound abstract, but it carries deep significance: sampling from this distribution is strongly believed to be classically intractable when the number of photons grows large​. In fact, the mathematical amplitudes for these photon scattering events are given by the permanent of large matrices, a calculation that is #P-hard (extremely difficult for classical computers)​. Scott Aaronson and Alex Arkhipov, who introduced the boson sampling model, showed that if a polynomial-time classical algorithm could simulate boson sampling, it would imply a collapse of the polynomial hierarchy in complexity theory (an unlikely scenario)​. This connection to computational complexity is why boson sampling is seen as a promising path to demonstrate a quantum advantage over classical computers, even though it is not a general-purpose quantum computer​.

In essence, boson sampling trades universality for feasibility. It does only one particular type of computation (sampling a bosonic distribution), but it does so with far fewer resources than a universal quantum computer would require​. Notably, a boson sampler does not need qubit entangling gates, adaptive circuits, or error correction. It relies only on three ingredients: (1) a source of identical single bosons (e.g. identical photons), (2) a passive linear-optical network, and (3) photodetectors to count the output​​. Photons are ideal bosons for such an experiment: they are relatively easy to generate, can propagate without interacting with the environment (thus maintaining coherence), and are straightforward to detect. This photonic implementation of boson sampling has been deemed one of the most practical routes toward showing quantum computational power in the near term​.

Why is this important? Boson sampling doesn’t break new ground in what computations can be done – it is not Turing-complete – but it provides a “shortcut” to quantum computing performance for a task believed to be classically unachievable​. It was proposed during the search for intermediate milestones like “quantum supremacy,” where a quantum device performs some computation exponentially faster than any classical computer (even if the task itself isn’t broadly useful). The boson sampling machine serves as an “analogue quantum computer” that uses the natural behavior of bosons to perform a hard sampling calculation, rather than logical qubits to perform arbitrary algorithms​​. In summary, boson sampling is a fascinating corner of quantum computing where quantum optics and computational complexity meet, offering a testbed for quantum advantage with current or near-term technology.

Key Academic Papers

Some of the most influential papers and results defining boson sampling and its evolution are listed below, with links to sources for further reading:

• Aaronson & Arkhipov (2011/2013) – “The Computational Complexity of Linear Optics“: This foundational theory paper introduced the boson sampling model. It argues that a device sending identical photons through a linear interferometer and sampling the outputs would perform tasks intractable for classical computers (under plausible complexity assumptions)​. This work laid the theoretical groundwork for boson sampling as a candidate for demonstrating quantum supremacy.
• Broome et al. (2013) – First Experimental Boson Sampling: One of the first experimental validations of boson sampling, using a photonic circuit with three indistinguishable photons. Broome and colleagues showed that the observed three-photon coincident detection rates matched the theoretical probabilities calculated via matrix permanents​. (Simultaneously, four independent groups, including Broome et al. and Spring et al., reported similar small-scale boson sampling experiments.) These 2013 results were a crucial proof-of-concept that boson sampling works as expected with real photons.
• Lund et al. (PRL 2014) – “Boson Sampling from a Gaussian State“: This proposal introduced the scattershot boson sampling approach, suggesting the use of Gaussian states (squeezed-light sources) instead of single photons to greatly improve experimental rate and scalability. It showed that an array of probabilistic photon-pair sources (parametric down-conversion) could be leveraged so that boson sampling can be performed more efficiently (by “heralding” the presence of photons). This idea bridged to what later became known as Gaussian Boson Sampling, using existing technology to make larger boson sampling experiments feasible.
• Hamilton et al. (2017) – “Gaussian Boson Sampling“: A landmark theory paper that formally defined Gaussian Boson Sampling (GBS) as a variant of boson sampling using squeezed vacuum states as inputs​. It showed that output probabilities in GBS are related to mathematical objects called hafnians (a generalization of the permanent)​, and it argued that GBS retains the computational hardness of standard boson sampling. This work provided a theoretical foundation for the later quantum advantage experiments that employed Gaussian input states.
• Zhong et al. (2020) – “Quantum Computational Advantage Using Photons“: This paper (from the USTC group in China) reported “Jiuzhang,” the first photonic boson sampling experiment to achieve quantum computational advantage. Using 50 indistinguishable squeezed-state inputs in a 100-mode interferometer, they observed up to 76 output photon clicks. The sampling rate of Jiuzhang was reported as 10^14 times faster than the best classical simulation attempt​. This was a breakthrough demonstration that a boson sampler can vastly outperform classical supercomputers for a specific task.
• Zhong et al. (2021) – “Phase-Programmable Gaussian Boson Sampling“: An upgrade on Jiuzhang, often dubbed “Jiuzhang 2.0,” which achieved 113-photon detection events out of a 144-mode circuit​arxiv.org​arxiv.org. This experiment improved source brightness and introduced some programmability (phase-tunable inputs). The results pushed the computational boundary even further, with an output space dimension of 10^43 and sampling 10^24 times faster than brute-force classical methods​arxiv.org. It showed that photonic quantum advantage is robust even as classical algorithms improve.
• Madsen et al. (2022) – “Quantum Computational Advantage with a Programmable Photonic Processor“: This was achieved by Xanadu’s “Borealis” device, a time-multiplexed 216-mode Gaussian boson sampler. Borealis introduced fully dynamic, programmable optical gates and obtained output samples with a mean of 125 photons detected (and up to 219 in some events)​. The team reported that obtaining one sample from Borealis took 36 microseconds, whereas the best-known classical simulation would take an estimated 9,000 years​. Borealis was the first photonic machine with programmable gates to demonstrate quantum advantage, addressing concerns about previous static setups and their vulnerability to classical spoofing.

(The above are just a few key papers. Many other important works exist, such as validation experiments​, studies on classical simulability​, and proposals to use boson sampling for applications. Interested readers can refer to comprehensive reviews and references within these papers.)

How It Works

Underlying Physics and Computation (Non-Gaussian Boson Sampling)

In a boson sampling device, we have M identical photons entering N input ports of a linear optical circuit (with N ≥ M). Each photon’s quantum state is described by the mode it occupies. The linear interferometer applies a fixed unitary transformation on these modes (e.g. mixing the paths via beam splitters). Because photons are bosons, their quantum amplitudes add coherently over all possible permutations of which output ports they could land in. When we measure how many photons ended up in each output mode, the probability of a specific detection pattern is given by the squared magnitude of a permanent of an M×M submatrix of the unitary matrix describing the interferometer​. Intuitively, each term in the permanent corresponds to one way the photons could propagate through the network and bunch among outputs, and because they are indistinguishable bosons, all those pathways interfere positively (unlike fermions which would have cancellations). Computing a permanent of a large matrix is a notorious #P-hard problem, which is why sampling from this distribution is believed to be classically intractable for large M​.

​A simple physical example of bosonic interference is the Hong-Ou-Mandel effect: if two identical photons enter a 50/50 beamsplitter from different sides simultaneously, they will bunch together and exit together in the same output port (either both in output A or both in output B) due to constructive interference of their two possible arrangements​. General boson sampling extends this idea to many photons and a complex network – the bosons’ tendency to coalesce and “stick together” in certain output configurations leads to output probabilities that are highly sensitive to the precise interferometer settings​​.

Crucially, for boson sampling to reflect the correct hard distribution, the bosons (photons) must be indistinguishable in all degrees of freedom (same frequency, polarization, arrival time, etc.). Any distinguishability or lost photons spoil the interference and typically make the distribution easier to simulate classically. Thus, experiments must carefully synchronize photons and filter their spectra. When done right, a boson sampler with even ~20–30 photons is already beyond brute-force classical simulation (since the dimension of the state space and number of terms in the permanents grows super-exponentially with photon number)​.

To use the device, one repeatedly triggers the photon sources and records the pattern of detection clicks. After many runs, one can estimate the probability distribution of different output configurations. Notably, boson sampling does not give a straightforward single answer – it produces samples from a distribution. Verifying that the device is working (and not secretly being spoofed by some classical strategy) involves statistical tests to compare the distribution of many samples against theoretical predictions or against alternative hypotheses (e.g. “maybe the photons were distinguishable or random”). This can be challenging because we cannot efficiently compute the ideal distribution for large instances; nonetheless, tests like cross-entropy benchmarking or comparing certain marginal distributions are used as evidence that the quantum device is sampling correctly.

Gaussian Boson Sampling (Using Squeezed States)

Gaussian Boson Sampling (GBS) is a variant of the protocol that uses Gaussian input states instead of single-photon Fock states. In quantum optics, “Gaussian” refers to states with Gaussian-shaped Wigner functions – typically states like coherent states or squeezed vacuum states. The original boson sampling (with single photons) uses highly non-Gaussian states (Fock states with a fixed photon number). Lund et al. (2014) observed that one can inject many squeezed vacuum states (which are Gaussian) into the interferometer and still obtain a hard-to-sample output distribution​. In a squeezed vacuum, photon pairs are created probabilistically; if one could “herald” (detect) one photon from a pair, the other is guaranteed and effectively behaves like a single photon input. This scattershot approach meant you don’t need M deterministic single-photon sources – you could have many probabilistic pair sources and use whatever subset actually produces photons in a given run​. Aaronson later showed that an unheralded Gaussian setup can be arranged that is complexity-wise equivalent to standard boson sampling: by delaying the herald detection to the end, one can embed a scattershot boson sampler within a Gaussian state experiment​​. In essence, Gaussian Boson Sampling can achieve the same hardness assumption as the original, while being friendlier to experimental implementation​.

In Gaussian boson sampling experiments like USTC’s Jiuzhang or Xanadu’s Borealis, the input to each interferometer mode is a squeezed state (often many modes are entangled via an initial optical network or a time-multiplexing loop). These inputs are Gaussian, and the interferometer itself is just linear optics (also Gaussian operations). If one were to only perform Gaussian measurements (like homodyne detection yielding continuous outcomes), the whole system would be Gaussian and efficiently simulable classically. The key is that the output measurement is non-Gaussian: typically, photon-number-resolving detectors are used at the outputs, which project the state onto discrete Fock outcomes (0,1,2,… photons in each mode). This non-Gaussian measurement is what makes the overall sampling task hard. The probability of getting a particular pattern of clicks in GBS is related to a matrix function called the hafnian (instead of permanent) of a matrix derived from the state’s covariance matrix​. Like the permanent, computing a hafnian for large matrices is #P-hard, so sampling from that distribution is believed to be intractable classically in the worst case​​.

Differences between Non-Gaussian and Gaussian boson sampling: In traditional (non-Gaussian) boson sampling, the number of photons M is fixed each run – e.g. you always inject exactly 20 single photons and expect 20 out at the outputs (ignoring losses). In Gaussian boson sampling, the total photon number detected can vary from run to run (since each squeezed mode can emit 0, 1, 2,… photons, with a bias towards 0). For example, in Jiuzhang, they had 50 squeezed modes and sometimes detected 70+ photons due to multiple pair emissions​. This variable photon-number scenario still yields a hard sampling problem, but it complicates direct comparison because one must consider a distribution over photon-number as well. GBS experiments often report the largest number of photons observed and the fact that this lies far beyond what classical simulation can handle​​.

Another difference is practical: Gaussian inputs (squeezers) are readily generated with pulsed lasers and nonlinear crystals, allowing dozens or hundreds of simultaneous mode inputs, whereas collecting dozens of on-demand single photons is technologically harder. The downside of GBS is that a lot of the time, many of those squeezed modes produce vacuum (no photon), contributing nothing to the sample except increasing combinatorial complexity. Nonetheless, GBS has become the go-to approach for photonic quantum advantage experiments because of its relative simplicity and scalability in hardware. Theory has reinforced that approximate GBS remains hard under the same complexity assumptions as standard boson sampling​.

In summary, both versions operate on the same principle of bosonic interference but use different quantum states. Non-Gaussian boson sampling uses a fixed number of non-Gaussian inputs (single photons) and directly implements the original Aaronson-Arkhipov protocol (permanent-based amplitudes). Gaussian boson sampling uses squeezed states (Gaussian inputs) and relies on measuring photon coincidences (hafnian-based probabilities). Each has its place: the original is conceptually cleaner, while the Gaussian variant is experimentally more convenient. Importantly, both are believed to be beyond classical reach when the scale is large enough​.

Comparison to Other Paradigms

Boson sampling is often discussed in contrast to other quantum computing paradigms, each with its own strengths and goals. Here we compare boson sampling to a few key paradigms:

• Vs. Universal Quantum Computing (Gate Model): A universal quantum computer (such as those using qubits and logic gates or adiabatic evolution) can perform any quantum algorithm, including Shor’s algorithm for factoring, Grover’s search, etc. Boson sampling, by contrast, is not universal – it performs one specific randomized computation. The upside is that boson sampling devices are much easier to build than a full universal quantum computer​. They don’t require qubit-qubit interactions, error-correcting codes, or adaptive logic. In a way, a boson sampler is more like an analogue computer solving one hard math problem (sampling from a distribution) using physics. Universal gate-model machines are far more powerful but also far more demanding in terms of coherence and control. For example, superconducting qubit processors (like Google’s Sycamore) need precise calibrated gate operations and cryogenic temperatures, whereas a boson sampler can operate at room temperature with passive optics. However, the gate model algorithms can tackle direct computational problems (like optimization or cryptanalysis) which boson sampling cannot directly address. One can say boson sampling is to a full quantum computer what a special-purpose hardware accelerator is to a general CPU: it’s extremely efficient for one task but cannot do arbitrary logic. In terms of quantum supremacy, boson sampling was one of the first proposed pathways to outpace classical computing, alongside random circuit sampling on qubit processors. Notably, Google’s 2019 supremacy demonstration used a random gate-circuit on 53 qubits, while USTC’s 2020 supremacy demonstration used boson sampling with photons – different hardware, same underlying goal of challenging the Extended Church-Turing Thesis (which says classical computers can simulate any reasonable physical computation efficiently).
• Vs. Adiabatic/Annealing Quantum Computing: Adiabatic quantum computing (and quantum annealers like D-Wave systems) is another non-universal paradigm, geared towards solving optimization problems by finding low-energy states of a programmable Hamiltonian. In an adiabatic machine, qubits represent spins in an Ising-model; the computation is an analogue process of gradually evolving the system so it hopefully lands in the optimal configuration. Boson sampling, on the other hand, does sampling rather than optimization. One could say boson sampling solves a “distribution sampling problem” whereas quantum annealers solve a “ground-state search problem.” Both are somewhat limited in scope compared to universal machines. A key difference: current annealers do involve tunable interactions between qubits (though in a restricted graph), whereas boson samplers have no direct photon-photon interactions – only the implicit interaction via bunching at beam splitters. From a complexity standpoint, certain optimization problems tackled by adiabatic machines are NP-hard (and it’s not proven a quantum annealer outperforms classical algorithms for them in general), whereas boson sampling’s hardness is rooted in #P-hard permanents and general complexity class arguments. In practice, annealers are being applied to specific tasks like protein folding or traffic flow optimization with mixed success, while boson sampling devices are so far dedicated to demonstrating a quantum advantage rather than solving practical problems. One might compare their technological status: quantum annealers with thousands of qubits exist (but they are noisy and limited in what they solve), whereas boson sampling experiments have achieved large photon numbers but with specialized outputs. Each provides lessons – for instance, both face the challenge of needing problem-specific verification (it’s hard to check if an annealer truly found the global optimum, just as it’s hard to verify a boson sampler’s distribution without heavy computation).
• Vs. Other Photonic Quantum Computing Approaches: Photonics is a popular platform for quantum computing in general, not just boson sampling. Linear optical quantum computing (LOQC), such as the Knill-Laflamme-Milburn (KLM) scheme, is a universal photonic approach that uses single photons, beam splitters, and additional ingredients like adaptive measurements and ancilla photons to enact two-qubit gates. KLM in principle allows universal computing, but it is hugely resource-intensive (requiring many extra photons and detectors for each gate)​. Boson sampling can be viewed as a simplified photonic computer that forgoes universality (no adaptive measurements or feed-forward) and thus drastically reduces resource requirements​. Another photonic approach is the one-way quantum computer using cluster states, where entanglement is created in a large photonic graph state and then consumed via measurements to perform computations. Companies like PsiQuantum and Xanadu are pursuing photonic architectures that eventually will do fault-tolerant universal computing, by generating large entangled optical networks. These approaches require on-demand sources and in some cases quantum memories or feed-forward switching, which are very challenging, but if achieved they could run any algorithm. Boson sampling, in contrast, does not require entangling gates beyond the natural interference of indistinguishable photons – the linear network itself doesn’t generate entanglement between separate photons (though multi-photon interference produces entanglement in the sense of output correlations).Within photonic computing, boson sampling is unique in that it doesn’t need nonlinear interactions or electro-optic modulation during the computation. The lack of dynamic control means boson samplers are mostly static circuits (except possibly phase tuning). That’s why Xanadu’s 2022 result was notable – Borealis introduced dynamically programmable beam splitter phases in time-multiplexed loops​, merging some benefits of universal photonic circuits with the boson sampling framework.
• Vs. Quantum Simulators (Analog): One could also compare boson sampling to other analog quantum simulators (like ultra-cold atom simulators or ion trap simulators), where the device is built to emulate a specific quantum model (such as the Hubbard model or Ising model). Those systems aim to solve specific physics problems (phase transitions, material properties) by directly mimicking the system of interest. Boson sampling, while an analog-ish device, is not simulating another physical system per se – it’s more directly implementing a mathematical problem (computing permanental probabilities) via a physical process. Both analog simulators and boson samplers fall under the umbrella of intermediate quantum technologies that can potentially outperform digital classical computation in their specialized domains. They also share the issue of verification: how do you verify the outcome of an analog quantum simulator of a many-body system? It’s akin to verifying boson sampler outputs – often one can only check certain smaller subsets or statistical properties.

In summary, boson sampling stands apart as a highly specialized quantum computing paradigm. It sacrifices generality for immediacy, leveraging the physics of non-interacting photons to tackle a computational task that is believed to be out of reach classically. Unlike universal or adiabatic quantum computers, you wouldn’t run conventional algorithms on a boson sampler. Its “output” is not the answer to a typical problem but rather a set of samples from a probability distribution. That being said, the conceptual successes of boson sampling have heavily influenced the discourse on quantum supremacy and provided a contrasting approach to gate-model experiments​. In practice, it complements other paradigms: for instance, a universal quantum computer could efficiently verify or even simulate boson sampling (since a universal quantum computer can simulate any quantum process, including photons), but building that universal machine is the harder problem. Boson samplers are here today (albeit in labs), showing a narrow but important slice of what quantum mechanics offers beyond classical computation.

Current Development Status

Boson sampling has progressed from a theoretical idea in 2011 to experimental reality by the mid-2010s, and more recently to a showcase of quantum computational advantage. Here’s a brief overview of its development status and milestones:

• Early Proof-of-Concept Experiments (2013–2015): Shortly after the proposal by Aaronson & Arkhipov, multiple groups raced to build small boson samplers. In 2013, four groups (in Brisbane, Oxford, Vienna, and Rome) demonstrated boson sampling with 3–4 photons in 3–9 mode interferometers​. For example, Broome et al. (2013) used 3 photons in a 6-mode interferometer on a silica chip​; Spring et al. (2013) used 4 photons in a fiber network; Tillmann and Crespi et al. (2013) each also reported 3-4 photon experiments using different photonic platforms. These experiments verified that the frequency of observing particular output patterns agreed with quantum predictions (based on permanents) and not with classical distinguishable-particle predictions​. While 3-4 photons are easily simulable classically, achieving even this required overcoming practical issues like photon loss and making photons indistinguishable. The takeaway from these early tests was positive: they showed “boson bunching” effects beyond classical correlation, and importantly, the errors (such as partial distinguishability) were not fatal​. Researchers could see a path to scaling up, at least in photon number if not yet in complexity.
• Scaling Up Photon Numbers (2015–2019): The next phase was to increase the number of photons and modes, while also improving source and detector technology. One strategy was scattershot boson sampling (as proposed by Lund et al.), which was experimentally realized in 2015 by Bentivegna et al. (2015)​. In scattershot experiments, many pair-photon sources are used and any that fire herald a single photon entering the interferometer, thereby in effect sampling a random subset of input ports each run. Bentivegna’s team used up to 6 photons distributed over 13 modes, and by taking advantage of many possible input combinations, they gathered evidence of correct boson sampling statistics with higher efficiency than a fixed-input approach​. By 2017–2018, experiments with 5 to 8 photons in 9-16 modes were reported, pushing into regimes where brute-force calculation of all probabilities was extremely demanding (though not impossible thanks to clever classical heuristics). During this time, there were also continuous improvements in integrated photonic circuits (for better phase stability and scaling to more modes) and in single-photon detectors (transition-edge sensors and superconducting nanowire detectors offering higher efficiency and some number resolution). A notable development was the use of quantum dot single-photon sources, which can emit photons on demand at high rates — in 2017, Wang et al. achieved 5 simultaneously generated indistinguishable photons from quantum dots and performed small boson sampling experiments, hinting that solid-state emitters might eventually scale up the photon numbers. Still, getting beyond ~8 photons remained challenging with pre-2020 technology, primarily due to probabilistic sources and loss.
• Quantum Advantage Experiments (2020–2022): A quantum leap (pun intended) came in late 2020 when the University of Science and Technology of China (USTC) team led by Jian-Wei Pan and Chao-Yang Lu unveiled Jiuzhang, a 100-mode Gaussian boson sampling experiment that detected up to 76 photons simultaneously​. This was a massive scale-up, achieved by using ultra-bright optical parametric oscillators as squeezed-light sources (25 double-pairs to make effectively 50 modes), a carefully phase-stabilized interferometer with 300+ optical elements, and superconducting nanowire detectors on all 100 outputs​. According to their analysis, Jiuzhang could generate ~10^14 samples in the time a classical supercomputer would take to generate one (or, put differently, what Jiuzhang did in minutes would take a classical simulation an estimated 2.5 billion years!)​. This stunning quantum advantage claim was published in Science and widely regarded as the photonic analog of Google’s earlier superconducting-qubit supremacy result​. In 2021, the USTC group followed up with Jiuzhang 2.0, improving their sources (through a “stimulated squeezing” technique) to reach 113-photon events in a 144-mode circuit​, and reported enhanced verification methods to ensure the results weren’t spoofable by classical means. The output distribution’s large size (Hilbert space dimension ~10^43) and the observed high-order correlations strongly suggested no efficient classical simulation existed at that scale​.

Around the same time, Toronto-based company Xanadu was also pushing GBS technology. In June 2022, Xanadu announced Borealis, which achieved quantum advantage with a different twist: Borealis is a time-multiplexed, dynamically programmable GBS machine​​. Instead of many spatial modes and sources, Borealis used three squeezed-light sources feeding a loop of fiber delays and programmable beam splitters, effectively creating 216 temporal modes that interfere with each other in a controlled way. This approach greatly reduces optical component count by reusing hardware in time, at the cost of more complexity in scheduling pulses. Borealis detected events with a mean photon count of 125 and up to 219 photons in certain samples​. They estimated a $9,000$-year gap between quantum and classical sampling for their parameters​. Importantly, Borealis introduced programmability — users (and the researchers) could adjust the implemented unitary easily by setting phase patterns on the loop’s switches for each pulse, whereas Jiuzhang’s interferometer was fixed once built. This addresses a criticism that early boson samplers were essentially static, one-off experiments. Xanadu has even made Borealis accessible over the cloud for researchers to run tasks, marking the first time a quantum advantage photonic device is available to external users.

• State of the Art: As of 2025, boson sampling experiments have firmly entered a regime beyond straightforward classical brute force. The largest instances have hundreds of modes and on the order of 10^2 photons. The primary limits to further scaling are losses (each photon has to survive through many beam splitters and be detected; as modes increase, the probability all photons survive decreases exponentially) and detector constraints (photon-number-resolving detectors with high efficiency are needed to fully utilize higher photon numbers, and they are still somewhat limited). Another ongoing race is between experimentalists and classical algorithm developers: whenever a new quantum milestone is reached, classical computer scientists devise better simulation or sampling algorithms to challenge the quantum advantage claim. For instance, after the Jiuzhang experiment, several papers improved the classical simulation of GBS using techniques like tensor networks and probabilistic methods, managing to simulate smaller versions or certain aspects of the experiment more efficiently than before (though still far from actually catching up to 76 photons). So far, quantum devices have maintained their edge by going to even larger scales or by improving the quality of the output such that classical spoofing strategies (which often rely on the presence of experimental noise) fail​.

In parallel, hardware improvements continue: better single-photon sources (e.g. quantum dot sources with higher purity and indistinguishability, or novel multiplexed down-conversion sources) are in development; detectors are improving in efficiency and timing; and integrated photonic circuits are getting more complex and low-loss. It’s reasonable to expect incremental growth in the scale of boson sampling over the next few years – perhaps 150+ photon events or use of even more modes. However, at some point mere scale-up may hit diminishing returns or practical limits (the experiment might become too costly or complex to stabilize). That is why researchers are also exploring hybrid approaches, such as combining photonic samplers with some feed-forward or using moderate nonlinearities to generate more complex entanglement that could broaden the computational power of these devices.

In summary, the current status of boson sampling is that it has evolved from a theoretical proposal to a demonstrated quantum advantage, albeit in a very specialized task. It serves as a benchmark for quantum photonic technology. The field now is balancing between making devices bigger and smarter versus finding new classical methods to simulate or verify those devices. Major players include academic groups (USTC, Oxford, Bristol, Vienna, etc.) and companies/startups (Xanadu, PsiQuantum, QuiX, Quandela among others) that are interested in photonic quantum computing. Notably, even companies focused on universal quantum computing keep an eye on boson sampling as a way to validate photonic components or to provide near-term quantum random generators. The progress so far suggests that boson sampling will remain a key part of the quantum computing landscape as we continue to test the boundaries of quantum advantage.

Advantages

Boson sampling devices, especially photonic ones, offer several notable advantages that have driven interest in this model:

• Relative Implementation Simplicity: Compared to universal quantum computers, a boson sampler is conceptually and physically simpler​. There’s no need for complex logic gates or qubit control sequences. Once the optical network is set up, the quantum evolution is just passive interference. This simplicity means less overhead – no need for adaptive measurements, feed-forward control, or ancillary qubits​. In practical terms, all the components required for boson sampling (laser sources, beam splitters, phase shifters, and photon detectors) are available with today’s photonic technology​. Indeed, one of the early selling points was that “all the elements required to build such a processor currently exist”​, making it a feasible experiment without waiting for new physics breakthroughs. This has been validated by multiple successful implementations on different photonic platforms.
• Room-Temperature Operation & Low Decoherence: Photons do not require cryogenic cooling – they can propagate in fiber or waveguides at room temperature with minimal decoherence. Unlike superconducting qubits or ion traps that often need temperatures near absolute zero or ultra-high vacuum, an optical boson sampler can, aside from perhaps the detectors, operate in normal lab conditions. Photons also interact only weakly with the environment, which means they can maintain quantum coherence over long distances and times​. This is a huge plus for scaling up: you can have a relatively large system (meters of optical fiber or many components on a chip) and not worry that the quantum state will decohere simply due to passage of time. The main source of error is loss, not decoherence – and loss can be somewhat managed by improving component quality.
• No Need for Two-Photon Interactions (Entangling Gates): In photonic quantum computing, the hardest task is usually to get photons to interact (since they don’t naturally interact with each other). Boson sampling sidesteps this by leveraging the bosonic nature of photons as the “engine” for computation. The interference at beam splitters, which causes effects like bunching, effectively generates the complicated output correlations without any direct photon-photon interaction​. One photon influences another’s behavior only via these interference effects, not via non-linear forces. This is advantageous because creating nonlinear interactions (like using special materials or quantum memories) often introduces additional loss and noise. By not requiring entangling gates, boson samplers avoid a major source of experimental complexity and error.
• Scalable in Modes (Passive Components): Increasing the number of modes N in an interferometer (more beam splitters and phase delays) is, in principle, easier than increasing the number of qubits in a gate-model device. Thanks to modern photonic integration, one can fabricate chips with dozens or even hundreds of waveguide modes, or use fiber loops to effectively create many temporal modes, without a huge increase in control complexity – the circuit is just fixed or periodically driven. This means one path to scale a boson sampler is simply use a bigger interferometer and more detectors. The challenge is mainly keeping losses low, but not necessarily an exponential increase in control lines or electronics. In contrast, adding more qubits to a superconducting chip, for example, often means a lot more microwave control lines, crosstalk issues, calibration, etc. Photonic systems can leverage multiplexing techniques (spatial, temporal, or frequency multiplexing) to simulate a very large network using fewer physical components (as demonstrated by Xanadu’s time-multiplexed Borealis)​. This kind of scaling is an advantage unique to photonics – you can trade time for hardware, using the same components repeatedly to emulate a large circuit.
• Speed of Operation: Photonic boson samplers can operate at high repetition rates. Lasers can pulse at rates of tens of MHz or more, and detectors can likewise reset at these rates (especially with modern superconducting detectors). This means such a device can produce many samples per second. In the quantum supremacy experiments, even though each sample isn’t solving a useful problem, the sheer rate of generating samples is a part of the advantage (billions of samples can be generated in minutes). If one imagines using a boson sampler for some application like random number generation, this high throughput could be very beneficial. Also, photonic computations are essentially as fast as light – the main latency is the transit through the optical circuit (often nanoseconds) and the detector response. There is no sequential logic gating that slows things down; many optical paths evolve in parallel at the speed of light. This massively parallel nature (all modes interfere simultaneously) and fast propagation means boson sampling is an ultra-fast analog computation happening in one shot.
• Energy Efficiency: Although not often highlighted as much as speed, photonic computing can be energy-efficient. Passive optical networks don’t consume energy (aside from a trivial amount of absorption). The main energy cost is in generating the photons (lasers) and in detecting them (which often results in a electrical pulse that needs amplification). Still, comparing running a photonic experiment that draws maybe a few kilowatts for lasers and cryocoolers, versus running a supercomputer for an equivalent task, the photonic approach can be orders of magnitude more energy-efficient for the same computational problem. This matters especially if boson sampling finds roles in tasks like Monte Carlo simulation or random number generation – one could get the result using far less energy than brute force classical simulation. The Jiuzhang experiment, for example, performed a task in minutes that was estimated to take millions of core-hours on classical hardware​. While an exact energy comparison wasn’t made, the implication is that nature (via quantum mechanics) was doing the heavy lifting of computation. As photonic technology improves, small optical chips might perform specific high-complexity computations using just LED lights and on-chip detectors, potentially offering energy advantages over power-hungry classical processors for those tasks.
• Near-Term Demonstration of Quantum Supremacy: From a scientific and strategic perspective, boson sampling’s biggest advantage is that it offered a near-term route to demonstrate quantum supremacy without waiting for full-scale quantum computers​en.wikipedia.org. This was indeed borne out: boson sampling experiments achieved quantum advantage in 2020, at a time when universal quantum computers could handle at most ~50 qubits with shallow circuits. This advantage is not just a stunt; it provides a valuable case study to test quantum hardware, understand sources of noise, and develop quantum-classical verification techniques. It’s a morale and momentum boost for the quantum field – a proof that quantum devices can outperform classical ones for certain well-defined tasks. For stakeholders in technology (including those in cybersecurity), this is a wake-up call that quantum computing is real, even if the task being solved is esoteric. It spurs investment and further research, leveraging photonics which is a mature industry (think fiber optics, photonic chips, etc.).
• Potential for Unique Applications: We’ll cover more in the “Impact on Cybersecurity” section and Future Outlook, but it’s worth noting that boson sampling isn’t just a dead-end demo. The complexity of its output distribution might be useful in its own right – for example, as a source of certified random numbers, as a primitive in certain cryptographic protocols, or even for solving specialized problems like molecular vibronic spectra or graph algorithms. Already, researchers have proposed that GBS can be used to tackle certain graph-related problems (e.g., finding dense subgraphs or predicting molecular docking configurations) by encoding those problems into a photonic network and analyzing the output samples. If these proposals bear fruit, a boson sampler could have practical advantages (speedups) for tasks that are hard for classical algorithms but map naturally to photonic sampling. While this is still speculative, it’s an encouraging sign that the platform might transcend its initial purpose.

In summary, boson sampling’s advantages stem from harnessing nature’s quantum behavior (interference of bosons) in a simple, direct way. It exemplifies the “hardware does the math” idea – the linear optical network physically implements a computation that would take enormous classical resources to simulate. The combination of technological readiness (photons are well-understood), passive stability, and fundamental computational hardness is what makes boson sampling an attractive approach in the quantum computing zoo.

Disadvantages

Despite its intriguing promise and recent successes, boson sampling comes with several significant drawbacks and challenges:

• Not a Universal Computer (Limited Utility): The most immediate limitation is that boson sampling doesn’t directly solve most problems of interest. It samples from a specific probability distribution but cannot be programmed to do arbitrary calculations or even to output a specific answer to a math problem. In practical terms, outside of demonstrating quantum advantage or generating randomness, it’s unclear what one can use a boson sampler for (more on that in the cybersecurity context and future outlook). Scott Aaronson himself half-joked about the question of “who cares?” if a quantum device is just solving a contrived problem to be hard​. Currently, boson sampling lacks a “killer app.” While proposals exist to apply it to things like quantum chemistry (vibronic spectra) or graph problems, these are either proof-of-principle or have classical alternatives. This disadvantage is essentially that boson sampling’s applicability is narrow – it was devised as a means to an end (showing quantum speedup), not to perform useful tasks. In contrast, a universal quantum computer or even an annealer has a roadmap (e.g. Shor’s algorithm for factoring, Grover’s for search, optimization tasks etc.). A photonic boson sampler that sits in a data center won’t directly replace any classical computing workload we currently have, apart from those related to its own problem domain.
• Scalability Challenges (Photon Sources and Loss): Although boson samplers avoid the hardest part of photonic QC (photon-photon gates), they introduce a different scalability challenge: the need for many high-quality photons simultaneously. The number of photons M is the critical parameter for complexity (roughly, classical difficulty grows super-exponentially with M). Generating M identical single photons at once is hard. Down-conversion sources are probabilistic and as M grows, the probability of getting M photons in one shot drops exponentially (this is why scattershot/GBS was introduced, to mitigate that). Quantum dot sources can produce photons on demand, but having dozens of them all emitting indistinguishable photons is an active research area and not yet routine. Even if you have M photons, sending them through an N-mode interferometer inevitably incurs loss – some photons will be absorbed or miss the detector. Loss is particularly bad for boson sampling: if even one of the M photons is lost or not detected, that output sample corresponds to a different scenario (fewer photons) and generally should be discarded or at least treated separately. High overall transmission (product of all component transmissions) is required; for example, Jiuzhang’s interferometer had to be ultra-low-loss to get meaningful 76-photon detection rates​. Each additional mode or component adds to loss, creating a trade-off between size and quality. This makes scaling photon number and mode number simultaneously a Herculean task. In short, boson samplers face a massive engineering challenge to go from ~100 photons to say 1000 photons. That exponential resource requirement (many sources, many detectors, all perfectly aligned) is a disadvantage compared to some qubit systems where adding one qubit only linearly increases complexity (though maintaining coherence is hard in those systems for other reasons).
• Noise and Errors Affect Complexity: Boson sampling’s classical hardness proofs assume ideal conditions: perfectly indistinguishable bosons and exact implementation of the unitary. In reality, any imperfection can potentially make the distribution easier to simulate. For instance, if photons are partially distinguishable (say they have slightly different wavelengths or timing), the output probabilities no longer involve permanents of full matrices, but something more factorable; in the extreme case of fully distinguishable photons, the output probabilities follow a much simpler distribution (no quantum interference)​. Likewise, if the interferometer isn’t truly random or has some structure, classical algorithms might exploit that. Noise in detectors (dark counts) or lost photons could allow an approximate simulation by assuming some fraction of photons were missing or by modeling a thermal mixture. Indeed, one classical strategy to “spoof” a noisy boson sampler is to generate samples from a distinguishable particle model or a biased random process that mimics some low-order statistics of the experiment – these are not exact but if the experiment itself is not ideal, it might be hard to tell the difference without enormous sample sizes. Therefore, boson sampling experiments have to operate in a regime where noise is low enough that no known efficient classical approximation applies. This is a delicate balance: some level of noise is inevitable in any real device, and pushing to more photons often means accepting more noise. It’s a disadvantage that boson sampling doesn’t have an error-correction scheme (unlike gate-model QC where error correction theoretically can reduce noise arbitrarily at the cost of overhead). If a boson sampler with 200 photons has even 5% loss per photon, the output distribution may deviate significantly from the ideal one – potentially enough that a clever classical algorithm can simulate that noisy distribution (even if the ideal one is hard). So the sensitivity to noise and lack of error correction is a key challenge.
• Verification Difficulty: Verifying that a boson sampler is doing what it’s supposed to do is non-trivial. How do you confirm that the device’s outputs indeed follow the quantum prediction (inaccessible to classical calculation for large M)? It’s a bit of a Catch-22: if we could fully verify the distribution efficiently, then we would have an efficient classical algorithm to simulate it, which contradicts the assumed hardness. So verification relies on indirect tests. For example, one can test symmetry properties, compare certain marginal distributions (like 1-photon or 2-photon correlations) to theoretical values, or test against simplified hypotheses (e.g., “are these samples consistent with just uniform random?” or “with distinguishable photons?”). While these tests can rule out blatant classical cheats and give confidence, they are not the same as a rigorous verification of each output’s probability. This is a disadvantage especially if one envisions using boson sampling for something in the real world – you’d want to know it’s correct. For quantum supremacy demonstrations, the community has accepted statistical evidence in lieu of exhaustive verification, but if boson samplers were ever to be used for say cryptographic purposes (like certified random number generation), developing a full verification protocol (possibly interactive or based on correlations that are easier to check) is needed. Some research does address this – e.g., looking at the so-called heavy output generation or cross-entropy benchmark (for random circuits) – but boson sampling has its own quirks, and verification remains partly an open problem. A cybersecurity specialist might see this as analogous to a system that’s believed secure but hasn’t been fully proven so, relying on assumptions that breaking it is hard. We assume the boson sampler is producing the right distribution because otherwise a complexity collapse happens, but that’s not the same as a proof.
• Resource Intensity for High Performance: While building a small boson sampler is easy, building one that truly outperforms classical computing by a wide margin took enormous effort and resources. For example, the Jiuzhang experiment employed a high-powered ultra-fast laser, dozens of nonlinear crystals, a vibration-stabilized optical network with hundreds of mirrors and beam splitters, and 100 high-efficiency detectors, all carefully synchronized​. The coordination and stabilization of so many components was a major achievement​. This suggests that as these experiments scale, the complexity (and cost) of the setup grows significantly. It’s not simply an issue of theory but of practical engineering: each additional photon source might require its own laser pumping, each additional detector its own electronics. The 2022 Xanadu approach mitigated some of this by multiplexing, but at the expense of requiring very fast low-loss optical switches and quantum memory in fiber loops. So either way, pushing further will demand cutting-edge engineering. This is a disadvantage in that boson sampling machines are not (yet) turnkey devices – they’re bespoke experiments. Contrast this with how classical computing scaled: one can buy thousands of CPUs or GPUs and run them in parallel relatively straightforwardly. For boson sampling, you can’t just buy 1000 photon sources off the shelf (yet) and plug them in – issues like indistinguishability and optical alignment mean a much more careful construction. Until there’s a robust photonic integration solution that combines sources, circuits, and detectors on one chip (something companies like PsiQuantum and others are indirectly working on), boson samplers at scale will remain finicky lab experiments.
• Exponential Output Space: Paradoxically, the same feature that gives boson sampling its power – the huge size of the output distribution – is also a handicap. With M photons in N modes, the number of possible output configurations (combinations of how those photons could be arranged) is astronomically large. For instance, Jiuzhang with up to 76 photons over 100 modes has on the order of 10^30 possible outcomes​. This means if you want to characterize the full distribution experimentally, you’re out of luck – you can never sample enough to even see all possible outcomes once, let alone get accurate probabilities for each. One typically can only sample a tiny fraction of the space. This is fine for supremacy (we only need to show classical computers can’t get even one sample easily), but if one wanted to use boson sampling for, say, generating a very specific probability distribution for a computation, you might need an enormous number of runs to accumulate useful statistics. In comparison, a gate-model quantum computer (though it has an exponentially large state space too) can be programmed to yield a specific answer with high probability for certain algorithms, which you then measure. Boson sampling doesn’t give you a straightforward bitstring output or a single answer; it gives you a spread of possibilities which you then interpret statistically. This “solution as a distribution” nature might limit practical applications. It also means if something goes slightly wrong in the setup, noticing it might require collecting a lot of data to catch subtle differences in distribution (unless the error has a distinctive signature).
• Competition from Improved Classical Algorithms: A more dynamic disadvantage is that boson sampling’s value hinges on classical intractability, but classical algorithms are not static. Computer scientists have developed better and better ways to simulate boson sampling approximately. For example, algorithms leveraging the structure of Gaussian states or using random hashing to approximate permanents have extended how many photons can be simulated on a given classical computer. While none have broken the advantage claimed by experiments yet, there’s a continuous back-and-forth: classical simulators improve, then experiments scale up or adjust to stay ahead​. This means boson sampling as a supremacy proof isn’t a done deal; one has to keep an eye on both sides. If someday someone finds a surprising classical algorithm that can simulate, say, 100-photon Gaussian boson sampling efficiently (perhaps by exploiting some previously unknown mathematical structure), that could suddenly diminish the perceived advantage of these photonic devices. In contrast, for universal quantum computing, we believe (though not proven) that problems like factoring are fundamentally hard for classical algorithms, so a quantum computer’s advantage there would be more assured (subject to those complexity assumptions). In boson sampling, the problem is also believed hard (related to permanents and hafnians), but since it was specifically chosen for expected hardness, a classical breakthrough in simulation would directly undermine the purpose. Thus there’s a bit of an arms race aspect which is a vulnerability: boson sampling doesn’t have an unconditional theoretical guarantee of hardness; it’s “likely hard” under certain conjectures, and those conjectures could be circumvented by new classical methods or if nature has more structure than we thought.

In summary, boson sampling devices are impressive but somewhat fragile in their scope. They demand nearly immaculate optical setups, yet deliver outputs that are hard to directly use. They excel at one thing (producing a complex distribution), and that one thing is hard to verify and currently of niche applicability. All these disadvantages are active areas of research: improving sources to address scalability, developing verification protocols, finding niche uses to overcome the utility issue, etc. For the cybersecurity-minded, one might view boson sampling as a one-way function of physics: easy to forward (just run the experiment), hard to invert (simulate classically). But like any such function, its security (here hardness) must be scrutinized against all potential attacks (classical algorithms, noise exploitation, etc.). As we’ll see next, this one-way nature could actually be turned into a positive in certain cryptographic contexts, even as it poses challenges in others.

Impact on Cybersecurity

At first glance, boson sampling might seem unrelated to cybersecurity – it doesn’t factor numbers or directly break cryptographic protocols. Indeed, boson samplers are not general quantum computers, so known quantum attacks (like Shor’s algorithm for breaking RSA) are not something they can perform. However, there are a few interesting angles by which boson sampling intersects with cybersecurity and cryptography:

• Quantum Randomness Generation: One valuable resource in security is high-quality randomness (for keys, nonces, one-time pads, etc.). Quantum processes are a superb source of true randomness. Boson sampling, in particular, produces very complex, hard-to-predict outcomes. If one trusts the quantum device, the output bitstrings (photon detection patterns) can be considered essentially random from the perspective of any classical adversary, because simulating or predicting them would require solving a hard problem (computing permanents or hafnians for a large random interferometer). This has led to the idea of certified randomness generation using quantum supremacy experiments. Aaronson and others have proposed protocols where a quantum sampling device (like a boson sampler or random circuit generator) can be used to generate random bits that are verifiably quantum – meaning a classical eavesdropper couldn’t predict them without breaking the assumed hardness problem​. The general approach is that the quantum device outputs some bits; one then uses a verification test (statistical) to ensure the output distribution has properties that only a genuine quantum process would produce. If the test passes, the outputs can be treated as certified random. In the context of boson sampling, one could imagine a setup where a company or service runs a boson sampler and produces random cryptographic keys. Even if someone had access to all the interferometer settings and classical description, they couldn’t feasibly predict the keys because that’s exactly the hardness – essentially leveraging the device as a kind of physical one-way function. This concept is still being refined, because fully device-independent randomness certification usually requires additional assumptions (like assuming the device is a non-communicating quantum system, etc.). But even a less strict scenario – where the quantum provider periodically tests their boson sampler’s output against distinguishable-photon hypotheses or other spoof tests – could give confidence that the bits are quantum-random. Companies like ID Quantique already use simpler quantum optic setups (like beam splitter quantum vacuum fluctuations) for random number generators, and boson sampling could be a future high-end method when very large bitstreams of certified randomness are needed​.
• Quantum-Secure Cryptographic Primitives: Boson sampling’s hardness has inspired proposals for new cryptographic primitives that would remain secure even against quantum adversaries (because even a quantum computer wouldn’t easily simulate a large boson sampler – it’s not an efficient algorithmic task for a standard quantum computer either, since that would basically require implementing the same optical process or computing permanents with a lot of gates). One example is treating a boson sampler as a kind of one-way function. In 2019, Nikolopoulos proposed a cryptographic one-way function based on boson sampling​. The idea is that one can define a function f(x) whose output is related to sampling outcomes of a certain boson sampling setup defined by input x (perhaps x encodes some phases or sources). Evaluating f(x) (i.e., running the photonic experiment or simulating it for small cases) is “easy” (for the quantum device), but inverting it – finding an input that produces a given sample pattern or distribution – is hard without essentially solving the boson sampling problem. Such a one-way function could be used in various cryptographic protocols (much like how the hardness of factoring is used in RSA, or discrete log in Diffie-Hellman). It’s important to note that this is theoretical so far; boson-sampling-based cryptographic schemes are not deployed and would need extensive security analysis. But research suggests boson sampling “may go beyond the proof of quantum supremacy and pave the way toward cryptographic applications”​. In simpler terms, if you trust that boson sampling is hard to spoof, you could use it as a building block for things like authentication or hash functions (where forging a pre-image would require solving that hard sampling problem).
• Physical Unclonable Functions (PUFs) and Authentication: A related concept is using the complexity of an optical scattering process (like boson sampling in a random medium) as a physical unclonable function. PUFs are hardware devices that have unique, unclonable characteristics, often used for device authentication – for example, a chip might have random manufacturing variations that make its response to electrical signals unique and unpredictable. A boson sampling device itself could act as a quantum PUF – because the exact way photons scatter in a complex optical network is effectively unique to that device and infeasible to duplicate or simulate. In fact, there was a proposal titled “Physical Unclonable Functions with Boson Sampling” (Garcia-Escartin, 2019) exploring this idea​. Imagine an optical token: a small photonic chip with a random network of beam splitters. When you send certain input photons into it, it produces a particular boson sampling output distribution characteristic of its internal random structure. No attacker could copy this chip’s behavior without literally replicating it atom-for-atom (which is infeasible) or solving an exponentially hard sampling problem. So it could serve as a unique identifier. To authenticate the device, a verifier could send a few different random inputs (or phases) and check that the statistics of outputs match what they should be for the legitimate device. Because even the device owner can’t compute those expected outputs (only measure them from the device), an adversary who steals the challenge-response data couldn’t simulate the device responses for new challenges without the actual device. In practice, classical optical PUFs exist (using speckle patterns of laser through a diffusive material), but a quantum version using indistinguishable photons might offer an extra layer of unpredictability and security (the “quantum” advantage being that even someone with a full blueprint and unlimited classical computing couldn’t simulate it). However, research also pointed out some flaws in early boson-sampling PUF schemes (some noted the scheme may be insecure if the adversary can get enough data), so this is an ongoing research area.
• Quantum-Resistant Cryptanalysis: Boson sampling is not known to significantly speed up attacking current cryptographic algorithms. Since it’s not a universal computer, it doesn’t run Shor’s algorithm (for breaking RSA/ECC) or Grover’s algorithm (for brute-force searching) or other such algorithms. Therefore, the arrival of boson sampling devices does not suddenly threaten existing public-key cryptosystems in the way a general quantum computer would. In fact, one could argue the opposite: boson sampling’s hardness assumption is closer to problems like the permanent, which are believed hard even for quantum computers (indeed, even a quantum computer would take exponential time to simulate a general boson sampling unless special structure is present). So boson sampling doesn’t aid a malicious actor in breaking codes; if anything, it provides new hardness assumptions that could be used to design cryptosystems. For example, one could conceive a cryptographic protocol where a public key is some description of a boson sampling setup and a private key is the ability to efficiently generate samples from it (i.e., having the physical device). An attacker with only classical resources would be unable to forge valid samples. This could lead to things like quantum tokens or quantum money ideas, where verification is done by sampling from a device. A crude example: a bank issues a “quantum credit card” that is actually a tiny boson sampler with a secret internal setting. To verify it’s genuine, the bank challenges it with some input or configuration, and it produces a sample that the bank’s classical systems couldn’t have produced on their own. These kinds of schemes blur into quantum authentication territory. There is ongoing theoretical work on quantum authentication of PUFs and related tasks​, indicating that boson sampling might play a role in future quantum-secured hardware.
• Cryptanalysis of New Schemes: One should note that any cryptographic use of boson sampling must be scrutinized for vulnerabilities. For instance, if someone designs a boson-sampling-based one-way function, cryptanalysts would try to find if approximate simulation or machine learning could invert it faster than brute force. Or if a PUF uses boson sampling, one must ensure an attacker cannot collect enough challenge-response pairs to build a surrogate model (maybe a neural network could approximate the PUF’s behavior without solving the full #P-hard problem in general, by exploiting the specific distribution of that PUF’s responses). These concerns mean that while boson sampling offers new hardness assumptions, they need to be conservative. A cautious approach in cybersecurity is to assume attackers will improve classical algorithms (just as we consider that one day attackers might have quantum computers for Shor’s algorithm). So any boson-sampling-based crypto should ideally remain secure even if an attacker has a modest quantum computer (since a large one could potentially directly simulate moderate boson sampling instances by brute force quantum simulation – albeit with a high gate count). It appears that the problems boson sampling is based on (like computing permanents) are indeed hard even for quantum computers (they belong to complexity classes believed to be out of reach of BQP, the class of problems solvable by quantum polynomial time). This is a good sign – it suggests boson-sampling-based crypto could be secure in a post-quantum world. But much more research is needed to turn these ideas into practical protocols with provable security guarantees under reasonable assumptions.
• Quantum Key Distribution (QKD) & Others: Boson sampling itself isn’t needed for QKD; simpler photonic schemes (like BB84 protocol with single photons) already accomplish quantum-secure key exchange. In fact, boson sampling is overkill for QKD and doesn’t fit the QKD protocol requirements. However, techniques developed for boson sampling – such as sources of indistinguishable photons or improved detectors – indirectly benefit QKD and other quantum cryptographic applications. For example, high-efficiency photon sources and detectors make QKD more secure and higher-rate. So one could say boson sampling’s development pushes photonics technology forward, which then has ripple effects on quantum communication security.

In summary, boson sampling’s primary cybersecurity relevance lies in its potential to provide new tools and assumptions for cryptography rather than breaking existing ones. It can be viewed as a natural complex one-way function generator: easy to produce a output (just run the optical experiment), hard to predict or reverse that output classically​. This makes it attractive for things like randomness beacons, cryptographic one-way functions, and hardware authentication tokens. We are still in early days – these ideas are largely on paper or in prototypes – but they illustrate that as quantum tech advances, even specialized devices like boson samplers could find a niche in the security landscape. For cybersecurity specialists, it’s worth keeping an eye on these developments. For instance, a secure random number service might one day advertise that it uses a photonic boson sampler to generate unpredictability, adding confidence against adversaries with large computing power (since even big classical computers can’t easily simulate what the device is doing). Or, conversely, if one sees a new cryptographic scheme claiming security because “no classical algorithm can spoof a boson sampling device”, a security expert would want to critically evaluate that claim – essentially entering the realm of post-quantum cryptography but with quantum devices in the mix in a novel way.

Future Outlook

The journey of boson sampling from a theoretical concept to experimental reality has been rapid, and its future is an open question that straddles both optimism and uncertainty. Here are some insights into where boson sampling might be headed, and its long-term viability and impact:

• Pushing the Technological Frontier: In the near future, we can expect continued efforts to scale boson sampling experiments to even larger systems. This includes increasing the number of modes and photons, improving photon sources (higher brightness, purity, and indistinguishability), and reducing losses. A key breakthrough would be the development of integrated photonic chips that incorporate sources, an interferometer, and detectors all in one package. Several teams are working on integrating single-photon sources (from quantum dots or nonlinear waveguides) with waveguide circuits. If successful, this could dramatically improve stability and reduce losses (no coupling light in and out of fibers, etc.). We might see boson samplers with >100 photon detection events routinely, and mode counts in the hundreds or thousands (especially using time multiplexing, which can create effectively very large interferometers using fiber delay lines, as Borealis did). Each increment will test the mettle of classical algorithms – maybe at 150 photons, even the best classical simulations struggle to approximate anything meaningful about the distribution. There is a sense that boson sampling experiments will serve as a benchmark test for classical computation: as quantum hardware ups the ante, classical supercomputers and algorithms try to keep up, and this competitive dynamic will clarify the boundary of quantum advantage more and more sharply​.
• Hybrid Approaches and Enhanced Capability: One path for the future is hybridizing boson sampling devices with some limited programmability or additional computation to broaden their use. For example, a boson sampler might be augmented with some classical post-processing or machine learning that interprets its samples to solve a problem (like a graph analysis problem). There is emerging research on using GBS for tasks like finding dense subgraphs or predicting molecular docking poses​. In these cases, one encodes the problem into a matrix that describes a Gaussian state or interferometer, then the boson sampler’s outputs are analyzed to extract an answer (maybe the frequency of certain output patterns relates to the solution). Early results showed, for instance, that GBS could be used to find approximate solutions to the NP-hard problem of finding perfect matchings in a graph by mapping graphs to photon correlation patterns​. In the next few years, we’ll likely see more experiments demonstrating application-motivated boson sampling – using smaller instances that can be partially verified classically, to show that even if it’s not faster yet, the quantum approach yields correct results for something like a molecular vibrational spectrum or a graph motif search. If any of these prove to have a quantum speedup at scale, boson sampling could graduate from purely “demo” status to a specialized quantum solver for certain problems.
• Integration into Quantum Computing Ecosystem: As universal quantum computing progresses (with superconducting qubits, trapped ions, etc.), one might wonder if boson sampling machines will become obsolete. After all, a fault-tolerant universal quantum computer could in principle simulate a boson sampling experiment by computing permanents or by literally simulating the linear optics (via the Reck decomposition of a unitary, etc.). However, that’s likely far off; quantum error-corrected computers with enough qubits to simulate 100-photon boson sampling might be decades away. In the meantime, boson sampling devices could find a place as special-purpose accelerators. It’s conceivable that future quantum data centers might have a variety of quantum hardware: gate-model processors for general algorithms, annealers for optimization, and photonic samplers for certain sampling and random generation tasks. Each would be used for what it’s best at. Boson sampling might also serve as a component in larger protocols – for example, as a source of certified randomness feeding into a cryptographic service, or as an entropy engine for Monte Carlo simulations. If one could interface a boson sampler with classical computers easily (perhaps via photonic chips that output bits to a computer), they could act as peripheral devices for randomness or for generating hard instances for classical algorithms (like a “quantum oracle” that produces challenge instances that classical solvers struggle with).
• Commercial and Industrial Adoption: Companies like Xanadu are already commercializing aspects of boson sampling. Xanadu’s cloud platform, for instance, offers access to Borealis for researchers. We might see a nascent Quantum Sampling as a Service – essentially cloud-based boson sampling devices that users can tap into for either studying quantum physics or attempting computations. In the next few years, if any of the application use-cases (like in machine learning or finance for random sampling tasks) prove advantageous, there could be startups or services offering photonic sampling for those niches. For example, one could imagine a service that uses a boson sampler to generate very high-quality large random graphs or networks for stress-testing algorithms or optimizing portfolios via some sampling method. This is speculative, but not far-fetched given the speed at which companies have adopted even the early, limited quantum computers for cloud services (IBM, Amazon Braket, etc., albeit those are gate-model). Photonic devices have the advantage of room-temperature operation and potentially easier maintenance, so a distributed network of boson samplers could be plausible. On the other hand, if universal quantum computing surges ahead and achieves, say, thousands of qubits and error correction within a decade, the focus might shift away from these intermediate models.
• Fundamental Science and New Physics: From a scientific perspective, building larger boson samplers could uncover interesting physics. For instance, highly multi-photon interference in large networks is a relatively unexplored regime. Questions about the transition from quantum to classical (as photons become partially distinguishable or as noise is introduced) can be probed. There may be new phenomena or pattern observations in boson sampling outputs (some papers discuss conjectures like the bosonic birthday paradox, computational complexity transitions, etc.). Also, techniques developed for boson sampling (like large low-loss interferometers) can be repurposed to other quantum photonic experiments, such as building cluster states for measurement-based quantum computing or simulating quantum walks that relate to different Hamiltonians. In this sense, the investments in boson sampling technology pay dividends across quantum science. We might also see boson sampling done in other physical systems: for example, using plasmons or phonons as the bosons instead of photons (though photons are easiest due to low decoherence). Any non-interacting bosonic system with controllable mode mixing could, in theory, demonstrate the same physics. This could lead to “boson sampling in optical fiber networks” at telecom wavelengths (leveraging existing fiber infrastructure) or “boson sampling in time-frequency domain” where the modes are frequencies of light rather than spatial modes. Each variant might offer practical conveniences (e.g., fiber networks for long-range boson sampling?).
• Long-Term Viability: In the long run, the fate of boson sampling likely depends on whether it finds a role beyond being a scientific curiosity. If universal quantum computers remain decades away and boson sampling is the only game in town to show quantum advantage, it will continue to garner interest and investment, as it has proven. If, however, universal quantum computing makes a breakthrough (like a clear path to fault tolerance), then the need for boson sampling as a standalone approach might diminish – why build a machine that only samples, if you can build one that computes everything including sampling? That said, even if universal machines come, boson samplers might still be simpler and thus much cheaper and easier to deploy for certain tasks (just as we still use analog special-purpose devices in some contexts even though digital computers can do everything, because the analog device might be faster or cheaper for that one job). Another factor is theoretical clarity: Researchers are trying to identify if boson sampling (or GBS) has any collapse in hardness for certain large but noisy regimes, or if it truly remains hard. If a strong theoretical result emerged that under realistic noise boson sampling is actually simulable, that would undercut its future. Conversely, if theory bolsters that even approximate boson sampling retains hardness up to certain noise thresholds, that encourages scaling up with confidence. So far, evidence points to the latter – it’s believed to remain hard as long as interference is maintained​.
• Boson Sampling to Quantum Computing Crossover: Some researchers view boson sampling experiments as stepping stones to full photonic quantum computing. For example, the technology to generate many photons and guide them through circuits will also be required for a photonic quantum computer that uses, say, Gaussian cluster states and photon measurements for universality. In fact, Gaussian boson sampling devices are basically producing large entangled Gaussian states (e.g., Borealis created a big entangled state of 216 modes). If one adds some non-Gaussian elements (like single-photon inputs or certain measurements) to such a state, it could become a universal quantum computer by the rules of continuous-variable quantum computing. Xanadu’s roadmap, for instance, might involve moving from GBS demonstrations to incorporating feed-forward and error correction to achieve universal computing eventually. So, one possible future is that boson sampling machines incrementally evolve – each new capability (programmability, feed-forward, etc.) moves them closer to general quantum computers. One day, the line may blur and we simply call them photonic quantum computers with the ability to do various algorithms, with boson sampling just one mode of operation among many. At that point, boson sampling as a distinct topic might fade, absorbed into the broader domain of photonic quantum computing.

In conclusion, the future of boson sampling is likely to follow a dual track: (1) as a continued testbed for quantum advantage and specialized applications, and (2) as a catalyst driving photonic quantum technology forward. Cybersecurity specialists and quantum computing enthusiasts alike should watch for developments such as the use of boson sampling in cryptographic protocols (showing a merging of quantum hardware and crypto), improvements in photonic integration (which could suddenly make these devices compact and robust), and any surprising uses of boson sampling in computational tasks (e.g., solving a problem faster than a classical heuristic by harnessing the boson sampler). While boson sampling devices won’t replace classical computers or even general quantum computers, they enrich the quantum ecosystem with a unique capability. The coming years will tell whether that capability remains largely a scientific showcase or becomes a tool with practical impact in computing and security. Either way, boson sampling has already secured its place in the history of quantum computing as the first approach to experimentally push beyond the reach of classical simulation in a convincing way​, and its ongoing story will continue to inform how we understand and leverage quantum computational power.