Every Benchmark All at Once

TL;DR

This paper reformulates five common randomized benchmarking techniques within the gate-set shadow tomography framework, enabling more efficient, comprehensive, and adaptable benchmarking of quantum devices with reduced sample sizes and enhanced noise characterization.

Contribution

It introduces a unified reformulation of randomized benchmarking methods using shadow tomography, improving efficiency and enabling detailed noise analysis with minimal experimental data.

Findings

Reduced gate-set size requirements for standard benchmarking

Lowered sample size via median-of-means estimators

Ability to reconstruct correlated noise and leakage errors

Abstract

As quantum technology matures, the efficient benchmarking of quantum devices remains a key challenge. Although sample-efficient, information-theoretic benchmarking techniques have recently been proposed, there is still a gap in adapting these techniques to contemporary experiments. In this work, we re-formulate five of the most common randomized benchmarking techniques in the modern language of the gate-set shadow tomography. This reformulation brings along several concrete advantages over conventional formulations of randomized benchmarking. For standard and interleaved randomized benchmarking, we can reduce the required gate-set size and, using median-of-means estimators, also reduce the required experimental sample size. For simultaneous and correlated randomized benchmarking, we can additionally reconstruct the Pauli-terms of correlated noise channels using additional post-processing of only a single experimental dataset. We also present a minimal approach to extract leakage errors. Our work provides a clear avenue for comprehensive, reliable, and convenient benchmarking of quantum devices, with all methods formulated under a single umbrella technique that can be easily adapted to a range of experimental quantities and gate sets.