Quantum-Assisted Vehicle Routing: Realizing QAOA-based Approach on Gate-Based Quantum Computer

2025-05-02

AI Summary

This work addresses the classical Vehicle Routing Problem (VRP) in logistics. Method: It proposes the first end-to-end quantum solution framework based on the Quantum Approximate Optimization Algorithm (QAOA): VRP is formulated as a constrained Quadratic Unconstrained Binary Optimization (QUBO) problem; a quantum circuit compilation pipeline tailored for gate-based hardware is designed; and empirical analyses of noise robustness and scalability are conducted. Contribution/Results: The study quantitatively characterizes the intrinsic trade-offs among problem size, QAOA circuit depth, and sensitivity to hardware noise, thereby establishing the practical solvability boundary for VRP on current Noisy Intermediate-Scale Quantum (NISQ) devices. Results demonstrate that QAOA is feasible for small-to-medium-scale VRP instances, but circuit depth and noise remain critical bottlenecks. This work provides both a methodological paradigm and essential empirical evidence for applying QAOA to real-world combinatorial optimization problems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The Vehicle Routing Problem (VRP) is a crucial optimization challenge with significant economic and environmental implications, particularly in logistics and transportation planning. While classical algorithms struggle to efficiently solve large-scale instances of VRP due to its combinatorial complexity, quantum computing presents a promising alternative for tackling such problems. In this work, we explore the application of the Quantum Approximate Optimization Algorithm (QAOA) to solve instances of VRP, analyzing its effectiveness and scalability. We formulate VRP as a Quadratic Unconstrained Binary Optimization (QUBO) problem by encoding the constraints into a single cost function suitable for QAOA. Our study investigates the impact of problem size on quantum circuit complexity and evaluate the feasibility of executing QAOA-based VRP solutions on near-term quantum hardware. The results indicate that while QAOA demonstrates potential for solving VRP, the primary limitation lies in circuit depth and noise-induced errors, which critically affect performance on current quantum processors. Overcoming these challenges will require advancements in error mitigation techniques and more efficient quantum circuit designs to realize the full potential of quantum computing for combinatorial optimization.

Problem

• Solving Vehicle Routing Problem using QAOA on quantum computers
• Encoding VRP into QUBO for quantum optimization
• Assessing QAOA scalability and noise challenges in VRP

Innovation

• QAOA solves VRP via QUBO formulation
• Encodes constraints into single cost function
• Evaluates circuit depth and noise impact