Estimating gate-set properties from random sequences

TL;DR

This paper introduces random sequence estimation, a method leveraging short, unstructured quantum gate sequences and classical post-processing to efficiently diagnose and characterize quantum gate-sets, including noise and cross-talk.

Contribution

It develops a new paradigm for quantum gate characterization using random sequences and robust shadow estimation techniques, enabling various tomography and noise learning tasks.

Findings

Close-to-optimal performance guarantees for shadow estimation.

Effective partial, compressive, and full process tomography.

Novel methods for gate optimization and cross-talk diagnosis.

Abstract

With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment - random sequence estimation - solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk.