标题： 一种新颖的蚁群优化量子算法 显示英文标题

标题： A Novel Quantum Algorithm for Ant Colony Optimization

摘要： 量子蚁群优化（QACO）因其结合了量子计算和蚁群优化（ACO）算法的优势，并克服了传统ACO算法的一些局限性，受到了广泛关注。然而，由于目前可用的量子计算机硬件资源有限，例如量子比特数量有限、高保真门操作缺乏以及抗噪能力低等问题，QACO的实际应用面临很大挑战。 本文通过将聚类算法与QACO算法相结合，提出了一种混合量子-经典算法，从而使这种扩展后的QACO能够处理大规模优化问题，使得基于现有量子计算资源的QACO实际应用成为可能。 为了验证算法的有效性和性能，我们以旅行商问题（TSP）为基准测试了开发的QACO算法。实验结果显示，在多个数据集下，该算法表现出色。此外，开发的QACO算法还展示了对计算过程噪声的鲁棒性，这通常是量子计算机实际应用的主要障碍之一。 我们的研究表明，将聚类算法与QACO相结合，有效拓展了当前NISQ（含噪声中等规模量子）时代下QACO的应用场景。 显示英文摘要

摘要： Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithms and overcomes some limitations of the traditional ACO algorithm. However, due to the hardware resource limitations of currently available quantum computers, such as the limited number of qubits, lack of high-fidelity gating operation, and low noisy tolerance, the practical application of the QACO is quite challenging. In this paper, we introduce a hybrid quantum-classical algorithm by combining the clustering algorithm with QACO algorithm, so that this extended QACO can handle large-scale optimization problems, which makes the practical application of QACO based on available quantum computation resource possible. To verify the effectiveness and performance of the algorithm, we tested the developed QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks. The developed QACO algorithm shows better performance under multiple data set. In addition, the developed QACO algorithm also manifests the robustness to noise of calculation process, which is typically a major barrier for practical application of quantum computers. Our work shows that the combination of the clustering algorithm with QACO has effectively extended the application scenario of QACO in current NISQ era of quantum computing.