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Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors

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arxiv 2201.09866 v2 pith:XNX66L4Z submitted 2022-01-24 quant-ph

Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors

classification quant-ph
keywords quantumnoisecancellationchannelscorrelatederrorestimateslarge
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Noise in pre-fault-tolerant quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation (PEC), which is an error-mitigation technique that effectively inverts well-characterized noise channels. Learning correlated noise channels in large quantum circuits, however, has been a major challenge and has severely hampered experimental realizations. Our work presents a practical protocol for learning and inverting a sparse noise model that is able to capture correlated noise and scales to large quantum devices. These advances allow us to demonstrate PEC on a superconducting quantum processor with crosstalk errors, thereby providing an important milestone in opening the way to quantum computing with noise-free observables at larger circuit volumes.

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Cited by 11 Pith papers

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