Hybrid Quantum-Classical Optimization for Multi-Objective Supply Chain Logistics

TL;DR

This paper presents a hybrid quantum-classical approach to solve complex multi-objective supply chain logistics problems formulated as QUBO, demonstrating high-quality solutions on quantum hardware.

Contribution

It introduces two novel hybrid solvers, IQTS and HBS, for real-world logistics optimization mapped onto quantum hardware.

Findings

Successfully mapped logistics problems onto quantum hardware

Achieved Pareto-optimal solutions with high quality

Demonstrated practical feasibility of quantum-enhanced optimization

Abstract

A multi-objective logistics optimization problem from a real-world supply chain is formulated as a Quadratic Unconstrained Binary Optimization Problem (QUBO) that minimizes cost, emissions, and delivery time, while maintaining target distributions of supplier workshare. The model incorporates realistic constraints, including part dependencies, double sourcing, and multimodal transport. Two hybrid quantum-classical solvers are proposed: a structure-aware informed tree search (IQTS) and a modular bilevel framework (HBS), combining quantum subroutines with classical heuristics. Experimental results on IonQ's Aria-1 hardware demonstrate a methodology to map real-world logistics problems onto emerging combinatorial optimization-specialized hardware, yielding high-quality, Pareto-optimal solutions.