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International Conference on Integrated Intelligence and Cognitive Engineering (ICIICE)

Analytical Dimensions and Performance Evaluation of the Quantum Approximate Optimization Algorithm

Blas Trigueros, Pedro Juan Roig, Salvador Alcaraz, Katja Gilly

Published Apr 18, 2026• 0 citations
Publisher
DOI
10.1109/iciice69672.2026.11565281

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to its expected robustness to systematic errors and the short circuit depth, it is one of the promising candidates likely to run on near-term quantum devices. We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exact or approximate solution. When comparing approximate solvers, their performance is characterized by the computational time taken to achieve a given quality of solution. Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality. In addition, we show that the QAOA performance varies significantly with the graph type. By selecting a suitable optimizer for the variational parameters and reducing the number of function evaluations, QAOA performance improves by up to 2 orders of magnitude compared to previous estimates. Especially for 3-regular random graphs, this setting decreases the performance gap with classical alternatives.