Hybrid Classical-Quantum Simulation of MaxCut using QAOA-in-QAOA

TL;DR

This paper presents a scalable hybrid classical-quantum approach using QAOA-in-QAOA for MaxCut problems, implemented on supercomputers, and compares its performance to classical algorithms like GW.

Contribution

It introduces a scalable implementation of QAOA-in-QAOA for MaxCut, demonstrating its performance on large-scale problems and analyzing its advantages over classical methods.

Findings

QAOA-in-QAOA shows advantages in certain problem instances.

The implementation is efficient and scalable on supercomputers.

Classical Goemans-Williamson algorithm still outperforms QAOA in tested cases.

Abstract

The Quantum approximate optimization algorithm (QAOA) is a leading hybrid classical-quantum algorithm for solving complex combinatorial optimization problems. QAOA-in-QAOA (QAOA^2) uses a divide-and-conquer heuristic to solve large-scale Maximum Cut (MaxCut) problems, where many subgraph problems can be solved in parallel. In this work, an implementation of the QAOA2 method for the scalable solution of the MaxCut problem is presented, based on the Classiq platform. The framework is executed on an HPE-Cray EX supercomputer by means of the Message Passing Interface (MPI) and the SLURM workload manager. The limits of the Goemans-Williamson (GW) algorithm as a purely classical alternative to QAOA are investigated to understand if QAOA^2 could benefit from solving certain sub-graphs classically. Results from large-scale simulations of up to 33 qubits are presented, showing the advantage of QAOA in certain cases and the efficiency of the implementation, as well as the adequacy of the workflow in the preparation of real quantum devices. For the considered graphs, the best choice for the sub-graphs does not significantly improve results and is still outperformed by GW.