Benchmarking neutral atom-based quantum processors at scale

TL;DR

This paper introduces scalable benchmarking methods for neutral atom quantum processors using QAA and QAOA algorithms on large problem instances, enabling performance comparison as quantum hardware advances.

Contribution

It presents two systematic, scalable benchmarks based on MIS problem solutions to evaluate and compare large neutral atom quantum processors.

Findings

Quer_aquila outperforms Pasqal_Fresnel on tested instances.

Benchmarks successfully scale up to 102 and 85 qubits.

Generated MIS instances up to 1000 qubits for future testing.

Abstract

In recent years, neutral atom-based quantum computation has been established as a competing alternative for the realization of fault-tolerant quantum computation. However, as with other quantum technologies, various sources of noise limit their performance. With processors continuing to scale up, new techniques are needed to characterize and compare them in order to track their progress. In this work, we present two systematic benchmarks that evaluate these quantum processors at scale. We use the quantum adiabatic algorithm (QAA) and the quantum approximate optimization algorithm (QAOA) to solve maximal independent set (MIS) instances of random unit-disk graphs. These benchmarks are scalable, relying not on prior knowledge of the system's evolution but on the quality of the MIS solutions obtained. We benchmark quera_aquila and pasqal_fresnel on problem sizes up to 102 and 85 qubits, respectively. Overall, quera_aquila performs better on QAOA and QAA instances. Finally, we generate MIS instances of up to 1000 qubits, providing scalable benchmarks for evaluating future, larger processors as they become available.