The traditional clustering problem of renewable energy profiles is typically formulated as a combinatorial optimization that suffers from the Curse of Dimensionality (CoD) on classical computers. To address this issue, this paper first proposed a kernel-based quantum clustering method. More specifically, the kernel-based similarity between profiles with minimal intra-group distance is encoded into the ground-state of the Hamiltonian in the form of an Ising model. Then, this NP-hard problem can be reformulated into a Quadratic Unconstrained Binary Optimization (QUBO), which a Coherent Ising Machine (CIM) can naturally solve with significant improvement over classical computers. The test results from a real optical quantum computer verify the validity of the proposed method. It also demonstrates its ability to address CoD in an NP-hard clustering problem.
@article{arxiv.2506.23569,
title = {Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer},
author = {Chengjun Liu and Yijun Xu and Wei Gu and Bo Sun and Kai Wen and Shuai Lu and Lamine Mili},
journal= {arXiv preprint arXiv:2506.23569},
year = {2026}
}