Benchmarking near-term quantum computers via random circuit sampling

TL;DR

This paper introduces a scalable, sampling-efficient algorithm for estimating total noise in general quantum circuits with non-Clifford gates, validated on IBM hardware, aiding noise characterization for near-term quantum devices.

Contribution

The authors develop a novel algorithm inspired by Google's quantum supremacy experiment that estimates total noise, including crosstalk, in non-Clifford circuits, addressing a key challenge in quantum hardware characterization.

Findings

Algorithm accurately estimates total noise in complex circuits.

Experimental validation on IBM Quantum hardware confirms effectiveness.

Supports assumptions of uncorrelated noise in certain quantum devices.

Abstract

The increasing scale of near-term quantum hardware motivates the need for efficient noise characterization methods, since qubit and gate level techniques cannot capture crosstalk and correlated noise in many qubit systems. While scalable approaches, such as cycle benchmarking, are known for special classes of quantum circuits, the characterization of noise in general circuits with non-Clifford gates has been an unreachable task. We develop an algorithm that can sample-efficiently estimate the total amount of noise induced by a layer of arbitrary non-Clifford gates, including all crosstalks, and experimentally demonstrate the method on IBM Quantum hardware. Our algorithm is inspired by Google's quantum supremacy experiment and is based on random circuit sampling. In their paper, Google observed that their experimental linear cross entropy was consistent with a simple uncorrelated noise model, and claimed this coincidence indicated that the noise in their device was uncorrelated -- a key step in hardware development towards fault tolerance. As an application, we show that our result provides formal evidence to support such a conclusion.