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

TL;DR

This paper introduces a practical method for learning and inverting sparse correlated noise models in large quantum circuits, enabling bias-free observable estimates on noisy quantum processors, demonstrated on superconducting devices.

Contribution

The work presents a scalable protocol for learning and inverting sparse Pauli-Lindblad noise models, capturing correlated errors and enabling effective probabilistic error cancellation on large quantum devices.

Findings

Successfully demonstrated PEC on a superconducting quantum processor with crosstalk errors.

Developed a scalable protocol for learning sparse correlated noise models.

Enabled bias-free estimates of physical observables in noisy quantum circuits.

Abstract

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.