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Boson Sampling from Gaussian States
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We pose a generalized Boson Sampling problem. Strong evidence exists that such a problem becomes intractable on a classical computer as a function of the number of Bosons. We describe a quantum optical processor that can solve this problem efficiently based on Gaussian input states, a linear optical network and non-adaptive photon counting measurements. All the elements required to build such a processor currently exist. The demonstration of such a device would provide the first empirical evidence that quantum computers can indeed outperform classical computers and could lead to applications.
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Cited by 1 Pith paper
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Generalized Cross-Entropy Benchmarking for Random Circuits with Ergodicity
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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