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arXiv:2102.10341 · Physical Review A

Simulating complex networks in phase space: Gaussian boson sampling

P. D. Drummond, Bogdan Opanchuk, Alexander S. Dellios, M. D. Reid

Published Jan 26, 2022• 33 citations
arXivHTMLPDF

Abstract

We show how phase-space simulations of quantum states in a linear photonic network permit the verification of measurable probabilities and entanglement. We compare our predictions with recent Gaussian boson sampling experiments of Zhong et al. These use squeezed inputs and efficient ``on-off'' detectors, with up to 76th-order measured coincidence counts in the data. We introduce a general definition of grouped ``on-off'' detection probabilities for this purpose. The positive-$P$ phase-space method is used to compute any grouped or marginal click probabilities. Additional decoherence is included to obtain agreement between theory and experiment. The only limitation in estimating grouped probabilities is the computational sampling error, which is similar in magnitude to the experimental sampling error. The results obtained and graphed here are from first-order up to 16 000th-order grouped count probabilities. However, any order between these is also computable. We extend these results to include grouped probabilities with multidimensional outcomes that have a polynomial number of points. We also analyze quadrature detection experiments and show how to simulate genuine multipartite entanglement using Wigner phase-space methods.

DOI
10.1103/physreva.105.012427

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We show how phase-space simulations of quantum states in a linear photonic network permit the verification of measurable probabilities and entanglement. We compare our predictions with recent Gaussian boson sampling experiments of Zhong et al. These use squeezed inputs and efficient on-off'' detectors, with up to 76th-order measured coincidence counts in the data. We introduce a general definition of grouped on-off'' detection probabilities for this purpose. The positive-PPP phase-space method is used to compute any grouped or marginal click probabilities. Additional decoherence is included to obtain agreement between theory and experiment. The only limitation in estimating grouped probabilities is the computational sampling error, which is similar in magnitude to the experimental sampling error. The results obtained and graphed here are from first-order up to 16 000th-order grouped count probabilities. However, any order between these is also computable. We extend these results to include grouped probabilities with multidimensional outcomes that have a polynomial number of points. We also analyze quadrature detection experiments and show how to simulate genuine multipartite entanglement using Wigner phase-space methods.
Abstract An important challenge with the current generation of noisy, large-scale quantum computers is the question of validation. Does the hardware generate correct answers? If not, what are the errors? This issue is often combined with questions of computational advantage, but it is a fundamentally distinct issue. In current experiments, complete validation of the output statistics is generally not possible because it is exponentially hard to do so. Here, we apply phase-space simulation methods to partially verify recent experiments on Gaussian boson sampling (GBS) implementing photon-number resolving detectors. The positive-P phase-space distribution is employed, as it uses probabilistic sampling to reduce complexity. It is ∼ 10 18 times faster than direct classical simulation for experiments on 288 modes where quantum computational advantage is claimed. When combined with binning and marginalization to improve statistics, multiple validation tests are efficiently computable, of which some tests can be carried out on experimental data. We show that the data as a whole has discrepancies with theoretical predictions for perfect squeezing. However, a modification of the GBS parameters greatly improves agreement for some tests. We suggest that such validation tests could form the basis of feedback methods to improve GBS experiments.
Abstract A growing cohort of experimental linear photonic networks implementing Gaussian boson sampling (GBS) have now claimed quantum advantage. However, many open questions remain on how to effectively verify these experimental results, as scalable methods are needed that fully capture the rich array of quantum correlations generated by these photonic quantum computers. In this paper, we briefly review recent theoretical methods to simulate experimental GBS networks. We focus mostly on methods that use phase-space representations of quantum mechanics, as these methods are highly scalable and can be used to validate experimental outputs and claims of quantum advantage for a variety of input states, ranging from the ideal pure squeezed vacuum state to more realistic thermalized squeezed states. A brief overview of the theory of GBS, recent experiments, and other types of methods are also presented. Although this is not an exhaustive review, we aim to provide a brief introduction to phase-space methods applied to linear photonic networks to encourage further theoretical investigations.
This thesis presents results under the topics of quantum correlations and quantum information processing using linear-optical networks. The first part of this thesis concerns with quantum correlations, specially in Gaussian states, which play a central role as resource in quantum protocols. The second part is about quantum information processing using linear-optical networks that are of great practical interest due to their simple physical realization. In the first part, we present a measurement-based method for verifying nonzero quantum discord, a measure of quantum correlations, in bipartite systems that can be used for both continuous and discrete-variable systems. Based on this method, we show that the only bipartite Gaussian states with zero quantum discord are product states. We also present a simple and efficient experimental method for verifying quantum correlations in Gaussian states using homodyne measurements. Moreover, we introduce an operational measure for quantifying quantum correlations in Gaussian states that is purely based on Gaussian measurements. We then illustrate the operational significance of the measure in terms of a Gaussian quantum protocol. In the second part, we present results on linear quantum-optics experiments, specifically boson sampling, an intermediate model of quantum computation that can be implemented using single-photon states, linear-optical networks, and photodetectors. We first present an efficient experimental method for characterization of linear-optical networks, which is essential for practical implementation of boson sampling. We show that using coherent states one can directly measure all the nontrivial phases and moduli of the matrix describing the network. Next, we consider the problem of boson sampling with Gaussian states. We derive a general formula for calculating the output photon-counting probabilities. We show that for input thermal states, sampling from the output probability distribution can be efficiently classically simulated. Using this, we find that permanents of positive-semidefinite Hermitian matrices can be approximated to within a multiplicative error in BPPNP. We also discuss boson sampling with squeezed vacuum states. Finally, we provide a sufficient condition for the efficient classical simulation of the general sampling problem using linear-quantum optics, which involves a multimode input quantum state to a linear-optical network and set of measurements at the output. This condition is based on the phase-space quasiprobability distributions, and suggests that negativity is a quantum resource. We apply this condition to implementations of boson sampling using single-photon or spontaneous parametric down-conversion sources. We show that above some threshold for loss and noise in the experiment, boson sampling is classically simulatable.
The search for new, application-specific quantum computers designed to outperform any classical computer is driven by the ending of Moore's law and the quantum advantages potentially obtainable. Photonic networks are promising examples, with experimental demonstrations and potential for obtaining a quantum computer to solve problems believed classically impossible. This introduces a challenge: how does one design or understand such photonic networks? One must be able to calculate observables using general methods capable of treating arbitrary inputs, dissipation, and noise. We develop complex phase-space software for simulating these photonic networks, and apply this to boson sampling experiments. Our techniques give sampling errors orders of magnitude lower than experimental correlation measurements for the same number of samples. We show that these techniques remove systematic errors in previous algorithms for estimating correlations, with large improvements in errors in some cases. In addition, we obtain a scalable channel-combination strategy for assessment of boson sampling devices.
Computational validation is vital for large-scale quantum computers. One needs computers that are both fast and accurate along with tests that encapsulate the complexity of such networks. Here, we utilize positive-P phase-space simulations of grouped count probabilities (GCPs) as a fingerprint for verifying multi-mode data from large Gaussian boson sampling (GBS) quantum computers that claim quantum computational advantage. Such simulations allow one to distinguish between true non-classical correlations generated by an experiment, and those obtained from a classical algorithm that replicates the photo-detection measurements. We use these methods to verify the outputs of a 144-channel GBS experiment, where we show data is far enough from the ideal ground truth distribution that it is beaten by a classical algorithm implemented using purely classical states, although it fails against a best-fit ground truth that includes decoherence. This gives evidence consistent with quantum computational advantage for a modified problem: a non-classical, partly thermalized ground truth model. Even with this model, discrepancies are present, indicating possible parameter estimation errors. Randomly generated tests from multiple high-order grouped counts are also performed. Each of these can be measured and simulated, providing a verification method that may be hard to replicate classically.