Abstract
Recently, the quantum computer’s ability to perform reliable computations beyond the capabilities of classical computing methods is referred to as quantum utility. To achieve this ability, it is becoming increasingly important to assess quantum algorithm performance in practical applications. Starting from this consideration, this work investigates the performance of the well-known Quantum Approximate Optimisation Algorithm (QAOA) with a Genetic Algorithm-based training in solving a real-world problem in the domain of power systems. As shown in the reported experiments using both an ideal simulator and a real IBM quantum processor, QAOA empowered by genetic algorithms outperforms the compared algorithms, particularly on real quantum hardware at the highest QAOA circuit depth.
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Chiatto, A., Alizadeh, A., Acampora, G., Vitiello, A., Pourabdollah, A., Lotfi, A. (2024). A Performance Study of Genetic Algorithms-Based Quantum Approximate Optimisation in the Context of Power Networks. In: Zheng, H., Glass, D., Mulvenna, M., Liu, J., Wang, H. (eds) Advances in Computational Intelligence Systems. UKCI 2024. Advances in Intelligent Systems and Computing, vol 1462. Springer, Cham. https://doi.org/10.1007/978-3-031-78857-4_23
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