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Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

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arxiv 2304.12240 v3 pith:4NQ6WG7V submitted 2023-04-24 quant-ph

Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

classification quant-ph
keywords bosongaussianquantumsamplesamplingadvantageclassicalcomputational
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years.

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Cited by 2 Pith papers

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

  1. Matrix product state approach to lossy boson sampling and noisy IQP sampling

    quant-ph 2025-10 accept novelty 6.0

    Lossy boson sampling and noisy IQP sampling are classically simulable with matrix product states, with the same known noise thresholds and accuracy controlled by bond dimension.

  2. Tensor Networks with Belief Propagation Cannot Feasibly Simulate Google's Quantum Echoes Experiment

    quant-ph 2026-04 unverdicted novelty 5.0

    Tensor networks with belief propagation fail to simulate Google's quantum echoes OTOC experiment because the circuits produce largely incompressible entanglement.