Benchmarking Metaheuristic-Integrated QAOA against Quantum Annealing

TL;DR

This paper benchmarks QAOA enhanced with metaheuristic optimizers against classical and quantum heuristics, revealing insights into their relative strengths, limitations, and potential for solving combinatorial optimization problems.

Contribution

It introduces a benchmarking framework for metaheuristic-integrated QAOA and provides comparative analysis with other heuristics, highlighting scenarios where hybrid approaches excel.

Findings

Metaheuristic QAOA outperforms classical heuristics on rugged landscapes.

Hybrid approach improves convergence speed of QAOA.

Guidelines for selecting optimization strategies based on problem characteristics.

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising Noisy Intermediate Quantum Algorithms (NISQ) in solving combinatorial optimizations and displays potential over classical heuristic techniques. Unfortunately, QAOA performance depends on the choice of parameters and standard optimizers often fail to identify key parameters due to the complexity and mystery of these optimization functions. In this paper, we benchmark QAOA circuits modified with metaheuristic optimizers against classical and quantum heuristics to identify QAOA parameters. The experimental results reveal insights into the strengths and limitations of both Quantum Annealing and metaheuristic-integrated QAOA across different problem domains. The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA, particularly for problems with rugged landscapes and limited quantum resources. Furthermore, the study provides guidelines for selecting the most appropriate approach based on the specific characteristics of the optimization problem at hand.