• 1 October 2025
    • preprint
    • Published by HAL in HAL
Abstract
In this paper, we propose a quantum approximate optimization algorithm (QAOA) for solving continuous-variable optimization problems on gate-based universal quantum computers. Our approach exploits the power of quantum computing to explore the solution space efficiently and find approximate optima. We demonstrate the effectiveness of our algorithm by solving a quadratic program, testing different combinations of mixer operators, numbers of repetitions and optimizers. The optimizer and the number of repetitions had the greatest effect size. The best performance was achieved using the SPSA optimizer, three repetitions, and the binary X mixer.
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