Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy

TL;DR

This paper introduces a hybrid quantum-classical approach using path-slicing strategies to improve the efficiency of solving the Traveling Salesman Problem on NISQ devices, demonstrating near-optimal solutions and resource savings.

Contribution

It presents novel path-slicing methods integrated with quantum local search, advancing quantum optimization techniques for large combinatorial problems.

Findings

Achieved near-optimal solutions on TSP instances from TSPlib.

Demonstrated significant improvements in solving efficiency and resource utilization.

Showcased the potential of hybrid quantum-classical strategies for complex optimization problems.

Abstract

We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current Noisy Intermediate-Scale Quantum (NISQ) technologies. Our hybrid quantum-classical approach leverages classical path initialization and quantum optimization to effectively manage the computational challenges posed by the TSP. We explore various path slicing methods, including k-means and anti-k-means clustering, to divide the TSP into manageable subproblems. These are then solved using quantum or classical solvers. Our analysis, performed on multiple TSP instances from the TSPlib, demonstrates the ability of our strategies to achieve near-optimal solutions efficiently, highlighting significant improvements in solving efficiency and resource utilization. This approach paves the way for future applications in larger combinatorial optimization scenarios, advancing the field of quantum optimization.