Evaluating the performance of quantum processing units at large width and depth

TL;DR

This paper introduces a scalable benchmarking protocol using LR-QAOA to evaluate large-width and depth quantum processors, providing a unified measure of their ability to maintain quantum coherence across diverse hardware.

Contribution

The paper presents the first extensive cross-platform quantum benchmarking protocol based on LR-QAOA, capable of assessing performance at large circuit depths and widths.

Findings

Benchmark tested 24 quantum processors from six vendors.

Circuits with up to 156 qubits and 10,000 layers were used.

The protocol effectively distinguishes hardware performance at large scales.

Abstract

Quantum computers have now surpassed classical simulation limits, yet noise continues to limit their practical utility. As the field shifts from proof-of-principle demonstrations to early deployments, there is no standard method for meaningfully and scalably comparing heterogeneous quantum hardware. Existing benchmarks typically focus on gate-level fidelity or constant-depth circuits, offering limited insight into algorithmic performance at depth. Here we introduce a benchmarking protocol based on the linear ramp quantum approximate optimization algorithm (LR-QAOA), a fixed-parameter, deterministic variant of QAOA. LR-QAOA quantifies a QPU's ability to preserve a coherent signal as circuit depth increases, identifying when performance becomes statistically indistinguishable from random sampling. We apply this protocol to 24 quantum processors from six vendors, testing problems with up to 156 qubits and 10,000 layers across 1D-chains, native layouts, and fully connected topologies. This constitutes the most extensive cross-platform quantum benchmarking effort to date, with circuits reaching a million two-qubit gates. LR-QAOA offers a scalable, unified benchmark across platforms and architectures, making it a tool for tracking performance in quantum computing.