Gaussian boson sampling validation via detector binning

• URL: http://arxiv.org/abs/2310.18113v2
• Date: Fri, 2 Feb 2024 14:00:29 GMT
• Title: Gaussian boson sampling validation via detector binning
• Authors: Gabriele Bressanini, Benoit Seron, Leonardo Novo, Nicolas J. Cerf and M.S. Kim
• Abstract summary: We propose binned-detector probability distributions as a suitable quantity to statistically validate GBS experiments. We show how to compute such distributions by leveraging their connection with their respective characteristic function. We also illustrate how binned-detector probability distributions behave when Haar-averaged over all possible interferometric networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaussian boson sampling (GBS), a computational problem conjectured to be hard to simulate on a classical machine, has been at the forefront of recent years' experimental and theoretical efforts to demonstrate quantum advantage. The classical intractability of the sampling task makes validating these experiments a challenging and essential undertaking. In this paper, we propose binned-detector probability distributions as a suitable quantity to statistically validate GBS experiments employing photon-number-resolving detectors. We show how to compute such distributions by leveraging their connection with their respective characteristic function. The latter may be efficiently and analytically computed for squeezed input states as well as for relevant classical hypothesis like squashed states. Our scheme encompasses other validation methods based on marginal distributions and correlation functions. Additionally, it can accommodate various sources of noise, such as losses and partial distinguishability, a feature that have received limited attention within the GBS framework so far. We also illustrate how binned-detector probability distributions behave when Haar-averaged over all possible interferometric networks, extending known results for Fock boson sampling.

Related papers

• Sample space filling analysis for boson sampling validation [0.0]
  We show that due to the intrinsic nature of the boson sampling wave function, its filling behavior can be computationally efficiently distinguished from classically simulated cases. We propose a new validation protocol based on the sample space filling analysis and test it for problems of up to $20$ photons injected into a $400$-mode interferometer.
  arXiv  Detail & Related papers  (2024-11-21T12:39:37Z)
• Linear cross-entropy certification of quantum computational advantage in Gaussian Boson Sampling [0.0]
  We argue that one can avoid this issue by validating GBS implementations using their corresponding ideal distributions directly. We explain how to use a modified version of the linear cross-entropy, a measure that we call the LXE score, to find reference values that help us assess how close a given GBS implementation is to its corresponding ideal model.
  arXiv  Detail & Related papers  (2024-03-22T16:49:17Z)
• Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers [143.6249073384419]
  In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
  arXiv  Detail & Related papers  (2024-01-29T02:08:40Z)
• Deterministic and Bayesian Characterization of Quantum Computing Devices [0.4194295877935867]
  This paper presents a data-driven characterization approach for estimating transition frequencies and decay times in a superconducting quantum device. The data includes parity events in the transition frequency between the first and second excited states. A simple but effective mathematical model, based upon averaging solutions of two Lindbladian models, is demonstrated to accurately capture the experimental observations.
  arXiv  Detail & Related papers  (2023-06-23T19:11:41Z)
• User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems [49.75149094527068]
  We show that diffusion models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. We develop a probabilistic approximation scheme for the conditional score function which converges to the true distribution as the noise level decreases. We are able to sample conditionally on nonlinear userdefined events at inference time, and matches data statistics even when sampling from the tails of the distribution.
  arXiv  Detail & Related papers  (2023-06-13T03:42:03Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Machine-Learned Exclusion Limits without Binning [0.0]
  We extend the Machine-Learned Likelihoods (MLL) method by including Kernel Density Estimators (KDE) to extract one-dimensional signal and background probability density functions. We apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $Z'$ boson decaying into lepton pairs.
  arXiv  Detail & Related papers  (2022-11-09T11:04:50Z)
• Validation tests of GBS quantum computers give evidence for quantum advantage with a decoherent target [62.997667081978825]
  We use positive-P phase-space simulations of grouped count probabilities as a fingerprint for verifying multi-mode data. We show how one can disprove faked data, and apply this to a classical count algorithm.
  arXiv  Detail & Related papers  (2022-11-07T12:00:45Z)
• A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data [71.9573352891936]
  This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data. Experimental results on synthetic data demonstrate that the proposed algorithm is robust to outliers and can be used with non-Gaussian data.
  arXiv  Detail & Related papers  (2022-01-28T10:01:37Z)
• Learnability of the output distributions of local quantum circuits [53.17490581210575]
  We investigate, within two different oracle models, the learnability of quantum circuit Born machines. We first show a negative result, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable. We show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable.
  arXiv  Detail & Related papers  (2021-10-11T18:00:20Z)
• Gaussian boson sampling with partial distinguishability [0.0]
  We investigate GBS with partial distinguishability using an approach based on virtual modes and indistinguishability efficiency. We show how the boundary of quantum supremacy in GBS can be pushed further by partial distinguishability.
  arXiv  Detail & Related papers  (2021-05-20T08:17:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.