Giving a convincing experimental evidence of the quantum supremacy over classical simulations is a challenging goal. Noise is considered to be the main problem in such a demonstration, hence it is urgent to understand the effect of noise. Recently found classical algorithms can efficiently approximate, to any small error, the output of boson sampling with finite-amplitude noise. In this work it is shown analytically and confirmed by numerical simulations that one can efficiently distinguish the output distribution of such a noisy boson sampling from the approximations accounting for low-order quantum multiboson interferences, what includes the mentioned classical algorithms. The number of samples required to tell apart the quantum and classical output distributions is strongly affected by the previously unexplored parameter: density of bosons, i.e., the ratio of total number of interfering bosons to number of input ports of interferometer. Such critical dependence is strikingly reminiscent of the quantum-to-classical transition in systems of identical particles, which sets in when the system size scales up while density of particles vanishes.
Giving a convincing experimental evidence of the quantum supremacy over classical simulations is a challenging goal. Noise is considered to be the main problem in such a demonstration, hence it is urgent to understand the effect of noise. Recently found classical algorithms can efficiently approximate, to any small error, the output of boson sampling with finite-amplitude noise. In this work it is shown analytically and confirmed by numerical simulations that one can efficiently distinguish the output distribution of such a noisy boson sampling from the approximations accounting for low-order quantum multiboson interferences, what includes the mentioned classical algorithms. The number of samples required to tell apart the quantum and classical output distributions is strongly affected by the previously unexplored parameter: density of bosons, i.e., the ratio of total number of interfering bosons to number of input ports of interferometer. Such critical dependence is strikingly reminiscent of the quantum-to-classical transition in systems of identical particles, which sets in when the system size scales up while density of particles vanishes.
To simulate noisy boson sampling approximating it by only the lower-order multi-boson interferences (e.g., by a smaller number of interfering bosons and classical particles) is very popular idea. I show that the output data from any such classical simulations can be efficiently distinguished from that of the quantum device they try to simulate, even with finite noise in the latter. The distinguishing datasets can be the experimental estimates of some large probabilities, a wide class of such is presented. This is a sequel of \textit{Quantum} \textbf{5}, 423 (2021), where I present more accessible account of the main result enhanced by additional insight on the contribution from the higher-order multi-boson interferences in presence of noise.
We study the quantum to classical transition in boson sampling by analyzing how N-boson interference is affected by inevitable noise in an experimental setup. We adopt the Gaussian noise model of Kalai and Kindler for boson sampling and show that it appears from some realistic experimental imperfections. We reveal a connection between noise in boson sampling and partial distinguishability of bosons, which allows us to prove efficient classical simulatability of noisy no-collision boson sampling with finite noise amplitude $\ensuremath{\epsilon}$, i.e., $\ensuremath{\epsilon}=\mathrm{\ensuremath{\Omega}}(1)$ as $N\ensuremath{\rightarrow}\ensuremath{\infty}$. On the other hand, using an equivalent representation of network noise as losses of bosons compensated by random (dark) counts of detectors, it is proven that for noise amplitude inversely proportional to total number of bosons, i.e., $\ensuremath{\epsilon}=O(1/N)$, noisy no-collision boson sampling is as hard to simulate classically as in the noiseless case. Moreover, the ratio of ``noise clicks'' (lost bosons compensated by dark counts) to the total number of bosons N vanishes as $N\ensuremath{\rightarrow}\ensuremath{\infty}$ for arbitrarily small noise amplitude, i.e., $\ensuremath{\epsilon}=o(1)$ as $N\ensuremath{\rightarrow}\ensuremath{\infty}$; hence, we conjecture that such a noisy boson sampling is also hard to simulate classically. The results significantly relax the sufficient condition on noise in network components, such as two-mode beam splitters, for classical hardness of experimental boson sampling
Boson Sampling is a prominent candidate for the demonstration of quantum computational advantage but it remains unclear whether a large-scale boson sampler can find useful computational applications. The challenge is that to use a boson sampler to estimate, for example, a physical observable, it is necessary to coarse grain the outcome distribution, owing to the exponential size of the outcome space and the anti-concentration properties of the Boson Sampling distribution. In this work, we analyse the complexity of a specific type of coarse-graining of Boson Sampling distributions based on linear functions of the output photon occupation numbers, which we refer to as linear statistics. We present an efficient classical algorithm for approximating linear statistics of boson samplers within additive error for different kinds of input states, such as Fock states and squeezed states. This allows us to unify in the same framework recent results on efficient quantum-inspired classical algorithms for simulating molecular vibronic spectra, or efficient classical approximations of coarse-grained distributions based on detector binning. Additionally, we show how our algorithm can be used to classically evaluate a proposed one-way function based on Boson Sampling, while other proposals for cryptographic applications escape our classical simulation techniques. We leave open the question of classical simulability of non-linear statistics and connect it to the problem of computing transition amplitudes of linear-optical circuits with one layer of interactions.
It is known that, by accounting for the multiboson interferences up to a finite order, the output distribution of noisy boson sampling, with distinguishability of bosons serving as noise, can be approximately sampled from in a time polynomial in the total number of bosons. The drawback of this approach is that the joint probabilities of completely distinguishable bosons, i.e., those that do not interfere at all, have to be computed also. In trying to restore the ability to sample from the distinguishable bosons with computation of only the single-boson probabilities, one faces the following issue: the quantum probability factors in a convex-sum expression, if truncated to a finite order of multiboson interference, have, on average, a finite amount of negativity in a random interferometer. The truncated distribution does become a proper one, while allowing for sampling from it in a polynomial time, only in a vanishing domain close to the completely distinguishable bosons. Nevertheless, the conclusion that the negativity issue is inherent to all efficient classical approximations to noisy boson sampling may be premature. I outline the direction for an enitre new program, which seems to point to a solution. However, its success depends on the asymptotic behavior of the symmetric group characters, which is not known.
It is known that, by accounting for the multiboson interferences up to a finite order, the output distribution of noisy Boson Sampling, with distinguishability of bosons serving as noise, can be approximately sampled from in a time polynomial in the total number of bosons. The drawback of this approach is that the joint probabilities of completely distinguishable bosons, i.e., those that do not interfere at all, have to be computed also. In trying to restore the ability to sample from the distinguishable bosons with computation of only the single-boson probabilities, one faces the following issue: the quantum probability factors in a convex-sum expression, if truncated to a finite order of multiboson interference, have, on average, a finite amount of negativity in a random interferometer. The truncated distribution does become a proper one, while allowing for sampling from it in a polynomial time, only in a vanishing domain close to the completely distinguishable bosons. Nevertheless, the conclusion that the negativity issue is inherent to all efficient classical approximations to noisy Boson Sampling may be premature. I outline the direction for a whole new program, which seem to point to a solution. However its success depends on the asymptotic behavior of the symmetric group characters, which is not known.
We present a classical algorithm that approximately samples from the output distribution of certain noisy Boson Sampling experiments. This algorithm is inspired by a recent result of Aharonov, Gao, Landau, Liu and Vazirani and makes use of an observation originally due to Kalai and Kindler that the output probability of Boson Sampling experiments with a Gaussian noise model can be approximated by sparse low-degree polynomials. This observation alone does not suffice for classical sampling, because its marginal probabilities might not be approximated by sparse low-degree polynomials, and furthermore, the approximated probabilities might be negative. We solve this problem by employing the first quantization representation to give an algorithm for computing the marginal probabilities of these experiments. We prove that when the overall noise rate is constant, the algorithm runs in time quasi-polynomial in the number of input photons N and accuracy. When the overall noise rate scales as 1−x1γ for constant x1 and γ=Ω(logN), the running time becomes polynomial. Furthermore, we study noisy Boson Sampling with practically relevant noise models such as partial distinguishability and photon loss. We show that the same technique does not immediately apply in these settings, leaving open the possibility of a scalable demonstration of noisy quantum advantage for these noise models in certain parameter regimes.