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

Abstract

Noise in quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation, 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 probabilistic error cancellation on a superconducting quantum processor, thereby providing a way to measure noise-free observables at larger circuit volumes.