A Novel Quantum Algorithm for Ant Colony Optimization
Qian Qiu, Mohan Wu, Qichun Sun, Xiaogang Li, Hua Xu

TL;DR
This paper introduces a hybrid quantum-classical algorithm combining clustering with quantum ant colony optimization to solve large-scale problems, demonstrating improved performance and noise robustness on the Traveling Salesman Problem.
Contribution
It presents a novel hybrid approach that extends the applicability of quantum ant colony optimization to larger problems within current quantum hardware constraints.
Findings
Better performance on multiple datasets
Robustness to quantum noise
Extended application scenarios for QACO
Abstract
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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research
