EURO 2025 Leeds
Abstract Submission

2764. Quantum Optimization for Supply Chain: QUBO Formulations and QAOA Solutions for Facility Location and Load Balancing

Invited abstract in session WC-16: Quantum Computing for Optimization, stream Quantum OR .

Wednesday, 12:30-14:00
Room: Esther Simpson 2.07

Authors (first author is the speaker)

1. Luis A. Moncayo-Martínez
Department of Engineering, Faculty of Environment, Science and Economy, The University of Exeter

Abstract

Quantum Bridge Analytics integrates classical and quantum computing to solve binary models by transforming mathematical programs into QUBO formulations. While tutorials exist on this process, key supply chain problems -facility location (FL) and load balancing (LB)- remain unaddressed. This work is among the first to apply QAOA to FL and LB, demonstrating its feasibility for real-world supply chain optimisation.
We developed a PennyLane Lightning-GPU-based application that takes FL and LB data as input. It converts these problems into QUBO, transforms them into an Ising model, and solves them via QAOA. For unbalanced penalisation, the objective function and constraints are modelled in DOcplex before transformation into an Ising model.
We tested the application on three instances of each problem. For FL, geodata from southwest England was used to determine optimal facility locations across nearby counties. For LB, real production line instances were solved, ensuring tasks were assigned to workstations while maintaining balanced workloads.
An analytics report is generated for each instance. Results indicate that the optimal solution is found using a variational approach to optimise circuit parameters. Increasing the number of shots enhances solution accuracy, closely matching results obtained via classical methods. The largest instance, a 30-qubit problem, is solved in approximately 20 seconds, demonstrating the potential of quantum computing for supply chain applications.

Keywords

Status: accepted

Back to the list of papers