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Gaussian Boson Sampling

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arxiv 1612.01199 v2 pith:32BAI64C submitted 2016-12-04 quant-ph

classification quant-ph
keywords bosongaussiansamplingstatesadvantagesphotonicprobabilityproblem
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Boson Sampling has emerged as a tool to explore the advantages of quantum over classical computers as it does not require a universal control over the quantum system, which favours current photonic experimental platforms.Here, we introduce Gaussian Boson Sampling, a classically hard-to-solve problem that uses squeezed states as a non-classical resource. We relate the probability to measure specific photon patterns from a general Gaussian state in the Fock basis to a matrix function called the hafnian, which answers the last remaining question of sampling from Gaussian states. Based on this result, we design Gaussian Boson Sampling, a #P hard problem, using squeezed states. This approach leads to a more efficient photonic boson sampler with significant advantages in generation probability and measurement time over currently existing protocols.

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Cited by 3 Pith papers

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

  1. $N$-Photon Amplitudes in EFT from Recursion Relations and Effective Vertices

    hep-th 2026-08 conditional novelty 7.0 of 10

    A CSW-like recursion plus a 'contact Lagrangian' computes arbitrary tree-level N-photon amplitudes in general EFTs, with results through 10 photons in Born-Infeld theory.

  2. Classical simulation and model concentration in passive linear optics

    quant-ph 2026-07 conditional novelty 6.0 of 10

    In passive linear optics, expectation-value concentration is set by misalignment of state and observable irrep purities, and known non-concentrating regimes remain largely classically tractable via irrep truncation or...

  3. Generalized Cross-Entropy Benchmarking for Random Circuits with Ergodicity

    quant-ph 2025-02 conditional novelty 5.0 of 10

    Random circuits satisfy an ergodicity condition for positive-coefficient polynomials, and its deviation can benchmark quantum chip fidelity, recovering and generalizing linear cross-entropy benchmarking.

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