A practical applicable quantum-classical hybrid ant colony algorithm for the NISQ era

TL;DR

This paper presents a hybrid quantum-classical ant colony algorithm that enhances large-scale optimization capabilities and demonstrates practical applicability on current noisy intermediate-scale quantum (NISQ) devices, especially for the TSP.

Contribution

The paper introduces a novel hybrid algorithm combining clustering with QACO, enabling practical large-scale optimization on NISQ-era quantum computers.

Findings

Improved performance on Traveling Salesman Problem benchmarks.

Effective handling of large-scale problems with current quantum resources.

Robustness against noise in NISQ devices.

Abstract

Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithm overcoming some limitations of the traditional ACO algorithm. However,due to the hardware resource limitations of currently available quantum computers, the practical application of the QACO is still not realized. In this paper, we developed a quantum-classical hybrid algorithm by combining the clustering algorithm with QACO algorithm.This extended QACO can handle large-scale optimization problems with currently available quantum computing resource. We have tested the effectiveness and performance of the extended QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks, and found the algorithm achieves better performance under multiple diverse datasets. In addition, we investigated the noise impact on the extended QACO and evaluated its operation possibility on current available noisy intermediate scale quantum(NISQ) devices. Our work shows that the combination of the clustering algorithm with QACO effectively improved its problem solving scale, which makes its practical application possible in current NISQ era of quantum computing.