Optimization Performance of QA and QAOA

This white paper surveys the research literature on quantum optimization solvers, specifically comparing the Quantum Annealing (QA) method implemented by D-Wave with the Quantum Approximate Optimization Algorithm (QAOA) that operates on gate-model quantum platforms. The document outlines empirical tests demonstrating that QA significantly outperforms QAOA on current-generation quantum platforms. It discusses the two approaches for solving hard optimization problems, detailing their methodologies and the differences in their quantum architectures. The paper presents findings from various research studies that confirm QA's superior performance in terms of solution quality and computation times. Additionally, it identifies theoretical challenges that may hinder QAOA's competitiveness, even on future quantum platforms. The conclusions drawn emphasize the ongoing dominance of QA in the quantum optimization landscape, supported by both empirical evidence and theoretical analysis.