Alleviating CoD in Renewable Energy Profile Clustering Using an Optical Quantum Computer

TL;DR

This paper introduces a quantum clustering method using an optical quantum computer to effectively address the Curse of Dimensionality in renewable energy profile clustering, demonstrating significant improvements over classical approaches.

Contribution

It proposes a novel kernel-based quantum clustering approach reformulating the problem as a QUBO solvable by a Coherent Ising Machine, validated on a real optical quantum computer.

Findings

Successfully encodes similarity into Ising model ground state

Addresses NP-hard clustering problem with quantum computing

Demonstrates improved performance over classical methods

Abstract

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.