Benchmarking quantum computers with any quantum algorithm

TL;DR

This paper introduces a scalable benchmarking method called subcircuit volumetric benchmarking (SVB) that assesses quantum computer performance on any quantum algorithm by analyzing subcircuits, providing a concise progress metric.

Contribution

The authors propose SVB, a scalable and efficient benchmarking approach that can be applied to any quantum algorithm or application, addressing limitations of existing benchmarks.

Findings

SVB accurately estimates quantum hardware capabilities.

Experiments on IBM Q systems validate SVB's effectiveness.

SVB provides a concise metric for progress towards utility-scale quantum computing.

Abstract

Application-based benchmarks are increasingly used to quantify and compare quantum computers' performance. However, because contemporary quantum computers cannot run utility-scale computations, these benchmarks currently test this hardware's performance on ``small'' problem instances that are not necessarily representative of utility-scale problems. Furthermore, these benchmarks often employ methods that are unscalable, limiting their ability to track progress towards utility-scale applications. In this work, we present a method for creating scalable and efficient benchmarks from any quantum algorithm or application. Our subcircuit volumetric benchmarking (SVB) method runs subcircuits of varied shape that are ``snipped out'' from some target circuit, which could implement a utility-scale algorithm. SVB is scalable and it enables estimating a capability coefficient that concisely summarizes progress towards implementing the target circuit. We demonstrate SVB with experiments on IBM Q systems using a Hamiltonian block-encoding subroutine from quantum chemistry algorithms.