Implementation of a Quantum Approximate Optimization Algorithm for Continuous Variables with Qiskit
Luna, Miguel, Vedant Patare, Gamzepelin Aksoy, Grégoire Cattan · HAL (Le Centre pour la Communication Scientifique Directe) · 2025
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.