GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

TL;DR

This paper presents a GPU-accelerated quantum computer simulator and investigates the relationship between quantum annealing and QAOA, revealing that a coarse approximation of QA can outperform QAOA in certain cases.

Contribution

Introduces a GPU-accelerated simulator for large-scale quantum applications and compares the effectiveness of approximate quantum annealing with QAOA.

Findings

AQA performs well compared to QAOA.

Coarse discretization in AQA can outperform QAOA.

Scaling results favor AQA for larger problem sizes.

Abstract

We study large-scale applications using a GPU-accelerated version of the massively parallel J\"ulich universal quantum computer simulator (JUQCS--G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs. Then, we use JUQCS--G to study the relation between quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). We find that a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), performs surprisingly well in comparison to the QAOA. It can either be used to initialize the QAOA, or to avoid the costly optimization procedure altogether. Furthermore, we study the scaling of the success probability when using AQA for problems with 30 to 40 qubits. We find that the case with the largest discretization error scales most favorably, surpassing the best result obtained from the QAOA.