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.
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.
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.
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.
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
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.
Bosonic many-body systems are prominent candidates for a quantum advantage demonstration, with the most popular approaches being either a quantum simulation beyond the reach of current classical computers, or a demonstration of boson sampling. It is a crucial open problem to understand how resilient such quantum advantage demonstrations are to imperfections such as boson loss and particle distinguishability. We partially solve this problem by showing that imperfect multi-boson interference can be efficiently approximated as ideal interference of groups of smaller number of bosons, where the other particles interfere classically. Crucially, the number of bosons undergoing interference in our approxmation only depends on the level of imperfections, but is independent of the actual number of particles. This allows us to construct a simple but stringent benchmark for comparing many-body bosonic technological platforms.