Robust shadow estimation

TL;DR

This paper introduces a noise-resilient enhancement to the shadow estimation protocol for quantum systems, enabling more accurate property estimation on noisy near-term quantum devices with minimal assumptions.

Contribution

It proposes a calibration-based robust shadow estimation method that improves accuracy and noise resilience over previous protocols in quantum property estimation.

Findings

Achieves unbiased estimates of quantum system properties.

Provides rigorous bounds on sample complexity.

Demonstrates improved performance through numerical experiments.

Abstract

Efficiently estimating properties of large and strongly coupled quantum systems is a central focus in many-body physics and quantum information theory. While quantum computers promise speedups for many such tasks, near-term devices are prone to noise that will generally reduce the accuracy of such estimates. Here we show how to mitigate errors in the shadow estimation protocol recently proposed by Huang, Kueng, and Preskill. By adding an experimentally friendly calibration stage to the standard shadow estimation scheme, our robust shadow estimation algorithm can obtain an unbiased estimate of the classical shadow of a quantum system and hence extract many useful properties in a sample-efficient and noise-resilient manner given only minimal assumptions on the experimental conditions. We give rigorous bounds on the sample complexity of our protocol and demonstrate its performance with several numerical experiments.