Quantum Annealing for Customer Segmentation: A Proof-of-Concept QUBO Framework for Supply Chain Clustering

This article investigates the potential of quantum clustering, specifi cally through quantum annealing, to enhance supply chain segmentation compared with classical K-Means. Using a proof-of-concept framework, clustering is translated into a Quadratic Unconstrained Binary Optimisation formulation, tested on customer datasets via both classical Simulated Annealing and D-Wave’s Leap hybrid solver, with penalty parameter optimisation introduced to address solver sensitivity. Performance is benchmarked against K-Means on runtime, accuracy metrics, and scalability, across dataset sizes from 200 to 5,000 points. Results show that while K-Means maintains superior inertia and stability, the hybrid quantum approach achieves faster solver-reported runtimes and, on a simplifi ed 200-point, two-feature benchmark, delivers improved silhouette, Davies-Bouldin, Calinski-Harabasz, and Dunn scores once penalties are optimised — metrics which, it is noted, reward the balanced partitions structurally favoured by the pairwise QUBO objective. However, scalability remains constrained by current qubit capacity and solver access, limiting practical deployment beyond mid-sized datasets. The study concludes that classical methods remain dominant for industrial-scale segmentation; the demonstrated framework, penalty-optimisation pipeline, and objective-level analysis nonetheless provide a documented foundation, grounded in a customer segmentation case study, upon which future quantum-assisted supply chain clustering can build as hardware matures.