一种用于蚁群优化的新型量子算法
A Novel Quantum Algorithm for Ant Colony Optimization
AI总结：
针对 NISQ 时代量子资源受限问题，提出结合聚类算法的混合量子-经典 QACO，并在 TSP 基准上验证其性能与噪声鲁棒性。
摘要：
量子蚁群优化（QACO）结合了量子计算与蚁群优化（ACO）算法的优势，并克服了传统 ACO 算法的一些局限，因此受到广泛关注。然而，由于当前可用量子计算机的硬件资源限制，例如量子比特数量有限、缺乏高保真门操作以及噪声容忍度较低，QACO 的实际应用颇具挑战性。本文提出一种混合量子-经典算法，将聚类算法与 QACO 算法相结合，使扩展后的 QACO 能够处理大规模优化问题，从而使基于现有量子计算资源实际应用 QACO 成为可能。为验证该算法的有效性和性能，作者以旅行商问题（TSP）作为基准，对所开发的 QACO 算法进行了测试。该 QACO 算法在多个数据集上表现出更优性能。此外，它还表现出对计算过程中噪声的鲁棒性，而噪声通常是量子计算机实际应用的主要障碍。本研究表明，将聚类算法与 QACO 相结合，有效扩展了 QACO 在当前 NISQ 量子计算时代的应用场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
