Exact Quantum Algorithm for Unit Commitment Optimization based on Partially Connected Quantum Neural Networks

2024/11/18 by Jian Liu, Liu, Jian, Xu Zhou +5 · 1 citation
Computer Science · Engineering · #Advanced Decision-Making Techniques #Engineering and Test Systems #FOS: Computer and information sciences #FOS: Physical sciences #Information Theory (cs.IT) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2411.11369

openalex publication_date 2024/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

The quantum hybrid algorithm has become a very promising and speedily method today for solving the larger-scale optimization in the noisy intermediate-scale quantum (NISQ) era. The unit commitment (UC) problem is a fundamental problem in the power system which aims to satisfy a balance load with minimal cost. In this paper, we focus on the implement of the UC-solving by exact quantum algorithms based on the quantum neural network (QNN). This method is tested with up to 10-unit system with the balance load constraint. In order to improve the computing precision and reduce the network complexity, we suggest the knowledge-based partially connected quantum neural network (PCQNN). The results show that the exact solutions can be obtained by the improved algorithm and the depth of the quantum circuit can be reduced simultaneously.

Cited by

• A Survey on Applications of Quantum Computing for Unit Commitment

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