An Advanced Hybrid Quantum Tabu Search Approach to Vehicle Routing Problems

2025-01-22

AI Summary

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) by proposing a Hybrid Quantum–Classical Tabu Search (HQTS) algorithm. HQTS dynamically integrates a D-Wave quantum annealer into the main tabu search loop for the first time, encoding each route’s sub-Traveling Salesman Problem (sub-TSP) as a Quadratic Unconstrained Binary Optimization (QUBO) instance and solving it on quantum hardware. It combines classical heuristic initialization with a CVRP/TSP decomposition strategy. The key innovation is a tunable-frequency “quantum routing” mechanism that enhances global exploration while overcoming the scalability limitations of purely quantum approaches. Experiments on standard CVRP benchmarks demonstrate that HQTS consistently yields optimal or near-optimal solutions, significantly reducing the optimality gap and outperforming existing hybrid algorithms. Moreover, higher quantum call frequency correlates strongly with improved solution quality and faster convergence.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Quantum computing (QC) is expected to solve incredibly difficult problems, including finding optimal solutions to combinatorial optimization problems. However, to date, QC alone is still far to demonstrate this capability except on small-sized problems. Hybrid approaches where QC and classical computing work together have shown the most potential for solving real-world scale problems. This work aims to show that we can enhance a classical optimization algorithm with QC so that it can overcome this limitation. We present a new hybrid quantum-classical tabu search (HQTS) algorithm to solve the capacitated vehicle routing problem (CVRP). Based on our prior work, HQTS leverages QC for routing within a classical tabu search framework. The quantum component formulates the traveling salesman problem (TSP) for each route as a QUBO, solved using D-Wave's Advantage system. Experiments investigate the impact of quantum routing frequency and starting solution methods. While different starting solution methods, including quantum-based and classical heuristics methods, it shows minimal overall impact. HQTS achieved optimal or near-optimal solutions for several CVRP problems, outperforming other hybrid CVRP algorithms and significantly reducing the optimality gap compared to preliminary research. The experimental results demonstrate that more frequent quantum routing improves solution quality and runtime. The findings highlight the potential of integrating QC within meta-heuristic frameworks for complex optimization in vehicle routing problems.

Problem

• Vehicle Routing Problem
• Capacity Constraints
• Optimization

Innovation

• Hybrid Quantum-Classical Tabu Search
• Quantum Computing Optimization
• Vehicle Routing Problem