Tight bounds on the convergence of noisy random circuits to the uniform distribution

• URL: http://arxiv.org/abs/2112.00716v3
• Date: Wed, 14 Sep 2022 19:48:33 GMT
• Title: Tight bounds on the convergence of noisy random circuits to the uniform distribution
• Authors: Abhinav Deshpande, Pradeep Niroula, Oles Shtanko, Alexey V. Gorshkov, Bill Fefferman, and Michael J. Gullans
• Abstract summary: We study the properties of output distributions of noisy, random circuits. We discuss recent barrier results for depth-agnostic and/or noise-agnostic proof techniques.
• Score: 1.4841630983274847
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the properties of output distributions of noisy, random circuits. We obtain upper and lower bounds on the expected distance of the output distribution from the "useless" uniform distribution. These bounds are tight with respect to the dependence on circuit depth. Our proof techniques also allow us to make statements about the presence or absence of anticoncentration for both noisy and noiseless circuits. We uncover a number of interesting consequences for hardness proofs of sampling schemes that aim to show a quantum computational advantage over classical computation. Specifically, we discuss recent barrier results for depth-agnostic and/or noise-agnostic proof techniques. We show that in certain depth regimes, noise-agnostic proof techniques might still work in order to prove an often-conjectured claim in the literature on quantum computational advantage, contrary to what was thought prior to this work.

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