Scalable Quantum Clustering of Renewable Profiles via a Hybrid Qubit-efficient Strategy

Abstract

Large-scale renewable energy profile clustering is a computationally challenging NP-hard problem. Recent quantum computing techniques offer a potential remedy, yet their scalability is severely constrained by the limited qubit count of current quantum hardware. To address this issue, we first cast the problem into a QUBO form and then we propose a consensus-based variable reduction (CBVR) method to drastically cut qubit usage. More specifically, we tailor the one-hot constraint to eliminate assignment variables and leverage consensus analysis to convert stable clustering relations from initial anneals into equality constraints for further reduction. Experiments on real quantum hardware confirm that CBVR compresses oversized QUBO models into feasible sizes while retaining high clustering quality and efficiency.