Evolving a multi-population evolutionary-QAOA on distributed QPUs

TL;DR

This paper introduces a hybrid evolutionary algorithm integrated with QAOA for improved combinatorial optimization, demonstrating enhanced accuracy and scalability on distributed quantum hardware for the Max-Cut problem.

Contribution

It presents a novel distributed multi-population EA strategy combined with QAOA, enabling scalable quantum optimization across multiple QPUs with classical communication.

Findings

Achieved equal or higher accuracy than traditional QAOA.

Reduced variance in solution quality using CVaR evaluations.

Validated approach on quantum simulators and IBM hardware.

Abstract

Our work integrates an Evolutionary Algorithm (EA) with the Quantum Approximate Optimization Algorithm (QAOA) to optimize ansatz parameters in place of traditional gradient-based methods. We benchmark this Evolutionary-QAOA (E-QAOA) approach on the Max-Cut problem for $d$-3 regular graphs of 4 to 26 nodes, demonstrating equal or higher accuracy and reduced variance compared to COBYLA-based QAOA, especially when using Conditional Value at Risk (CVaR) for fitness evaluations. Additionally, we propose a novel distributed multi-population EA strategy, executing parallel, independent populations on two quantum processing units (QPUs) with classical communication of 'elite' solutions. Experiments on quantum simulators and IBM hardware validate the approach. We also discuss potential extensions of our method and outline promising future directions in scalable, distributed quantum optimization on hybrid quantum-classical infrastructures.