One of the crucial steps in building a scalable quantum computer is to identify the noise sources which lead to errors in the process of quantum evolution. Different implementations come with multiple hardware-dependent sources of noise and decoherence making the problem of their detection manyfoldly more complex. During the presentation, we introduce a new randomized benchmarking algorithm which uses Weyl unitaries to efficiently identify and learn a mixture of error models which occur during the computation. We provide an efficiently computable estimate of the overhead required to compute expectation values on outputs of the noisy circuit. We show that the overhead decreases with the noise rate and this enables us to compute analytic noise thresholds that imply efficient classical simulability. Finally, we illustrate our methods by applying them to ansatz circuits that appear in the Variational Quantum Eigensolver and establish an upper bound on classical simulation complexity as a function of noise, identifying regimes when they become classically efficiently simulatable.
Zoom meeting details
Topic: Quantum Information and Quantum Computing Working Group
Time: October 15, 2020, 4:00 PM Warsaw
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Meeting ID: 94552545103
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