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Gaussian boson sampling validation via detector binning

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arxiv 2310.18113 v2 pith:P337SSYA submitted 2023-10-27 quant-ph

classification quant-ph
keywords distributionssamplingbosonclassicalbinned-detectorexperimentsgaussianprobability
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Experimental validation of boson sampling using detector binning

    quant-ph 2025-02 conditional novelty 7.0 of 10

    Binned-mode photon-count distributions validate three-photon boson sampling over 50 random interferometers, and their Haar-averaged variance is proportional to the sum of squared photon overlaps.

  2. Simulating lossy and partially distinguishable quantum optical circuits: theory, algorithms and applications to experiment validation and state preparation

    quant-ph 2024-12 conditional novelty 7.0 of 10

    The authors introduce a blocked loop Hafnian and finite-difference sieve that compute coarse-grained photon-number distributions of Gaussian states in exponential, not combinatorial, time.

  3. From the Hong-Ou-Mandel Effect to Quantum Sensing: Interference of Nonclassical Light with Partial Distinguishability and Noise

    quant-ph 2026-07 accept novelty 6.5 of 10

    New Fock-state suppression laws, a partial-distinguishability extension of Gaussian Boson Sampling via overlap matrices, and a proof that measurement incompatibility survives even when probe incompatibility vanishes f...

  4. Error Mitigation in Bosonic Systems via Virtual Distillation

    quant-ph 2026-07 accept novelty 6.0 of 10

    Passive linear interferometers implement virtual distillation for bosonic observables, recovering noise-suppressed number, phase-shift and quadrature expectations under loss and dephasing.

  5. Beyond Boson Sampling: Higher Spin Sampling as a Practical Path to Quantum Supremacy

    quant-ph 2025-05 conditional novelty 5.0 of 10

    For spin-S sampling, the number of sites m needed to match the hardness of Fock-state boson sampling scales as m ~ n^(1+3/(2S)), a large reduction from the spin-1/2 quartic scaling.

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