Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way

TL;DR

This paper introduces the Atos Q-score, a new application-centric, hardware-agnostic benchmark for quantum co-processors that measures the effective number of qubits for solving MaxCut problems, scalable to large quantum processors.

Contribution

The paper presents the Q-score benchmark, which is scalable, hardware-agnostic, and based on solving MaxCut with the Quantum Approximate Optimization Algorithm, providing a new standard for quantum processor evaluation.

Findings

Q-score effectively measures qubit utility for MaxCut problems.

Simulations show Q-score's robustness in noisy and ideal conditions.

Open-source implementation facilitates broad adoption.

Abstract

Existing protocols for benchmarking current quantum co-processors fail to meet the usual standards for assessing the performance of High-Performance-Computing platforms. After a synthetic review of these protocols -- whether at the gate, circuit or application level -- we introduce a new benchmark, dubbed Atos Q-score (TM), that is application-centric, hardware-agnostic and scalable to quantum advantage processor sizes and beyond. The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut combinatorial optimization problem with the Quantum Approximate Optimization Algorithm. We give a robust definition of the notion of effective performance by introducing an improved approximation ratio based on the scaling of random and optimal algorithms. We illustrate the behavior of Q-score using perfect and noisy simulations of quantum processors. Finally, we provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware.