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Center for Nonlinear Studies |
Error mitigation in a noisy quantum device requires a very good estimate of the noise channel. The accuracy of Probabilistic Error Cancellation is often limited by the high sample complexity of channel tomography. In principle, optimal sample complexity is attained by maximum likelihood estimation (MLE), but MLE is computationally challenging. We show that MLE can be made computationally tractable in certain cases of interest. For the common case of a 1D-local sparse Pauli-Lindblad channel, the likelihood function reduces to an efficiently-evaluable Bayesian network. We demonstrate improved error mitigation in this setting and discuss possible extensions.
Bio: Daniel Belkin is a PhD student at the University of Illinois. He received his B.A. from Swarthmore College in physics with minors in engineering and mathematics in 2019. He proceeded to research neuromorphic computing at the University of Massachusetts and computational plasma physics at Lawrence Berkeley National Lab before joining Clark Research group at UIUC to study quantum information.
Meeting Link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZTE0NzcxZjAtNmIyMi00ZTgzLWE5ZTUtYmQ0YWQ4OGEzNTQ3%40thread.v2/0?context=%7b%22Tid%22%3a%225216f00a-5b2e-4784-b2d3-ed19b748fd60%22%2c%22Oid%22%3a%2262e6fee7-00c9-460f-8b46-de3d6268aa2b%22%7d
Host: Paolo Braccia (T-4)