Quantum evolutionary algorithm for TSP combinatorial optimisation problem
<|reference_start|>Quantum evolutionary algorithm for TSP combinatorial optimisation problem: This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA). We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA). The TSP is a well-established challenge in combinatorial optimization where the objective is to find the most efficient path to visit a series of cities, minimizing the total distance, and returning to the starting point. We chose the TSP to test the performance of both algorithms because of its computational complexity and importance in practical applications. We choose the dataset from the international standard library TSPLIB for our experiments. By designing and implementing both algorithms and conducting experiments on various sizes and types of TSP instances, we provide an in-depth analysis of the accuracy of the optimal solution, the number of iterations, the execution time, and the stability of the algorithms for both. The empirical findings indicate that the CGA outperforms the QGA in terms of finding superior solutions more quickly in most of the test instances, especially when the problem size is large. This suggests that although the principle of quantum computing provides a new way to solve complex combinatorial optimisation problems, the implementation of quantum phenomena and the setting of parameters such as the optimal angle for a quantum revolving gate is challenging and need further optimisation to achieve the desired results. Additionally, it is important to note that the QGA has not been tested on real quantum hardware, so its true performance remains unverified. These limitations provide rich opportunities for further research in the future.<|reference_end|>
@article{ma2024quantum,
    title={Quantum evolutionary algorithm for TSP combinatorial optimisation
  problem},
    author={Yijiang Ma, Tan Chye Cheah},
    journal={arXiv preprint arXiv:2409.13788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13788},
    primaryClass={quant-ph cs.NE}
}