中国物理B ›› 2025, Vol. 34 ›› Issue (10): 100303-100303.doi: 10.1088/1674-1056/adf4aa

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Exact quantum algorithm for unit commitment optimization based on partially connected quantum neural networks

Jian Liu(刘键)2,†, Xu Zhou(周旭)1,3,6,†,‡, Zhuojun Zhou(周卓俊)2, and Le Luo(罗乐)1,4,5   

  1. 1 School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai 519082, China;
    2 QUDOOR Co., Ltd., Hefei 230000, China;
    3 QUDOOR Co., Ltd., Beijing 100089, China;
    4 Shenzhen Research Institute of Sun Yat-Sen University, Shenzhen 518057, China;
    5 Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing, Sun Yat-Sen University, Zhuhai 519082, China;
    6 Yangtze Delta Industrial Innovation Center of Quantum Science and Technology, Suzhou 215000, China
  • 收稿日期:2024-12-02 修回日期:2025-07-14 接受日期:2025-07-28 发布日期:2025-09-29
  • 通讯作者: Xu Zhou E-mail:zhoux359@mail.sysu.edu.cn
  • 基金资助:
    This work was supported in part by the China Postdoctoral Science Foundation (Grant No. 2023M740874).

Exact quantum algorithm for unit commitment optimization based on partially connected quantum neural networks

Jian Liu(刘键)2,†, Xu Zhou(周旭)1,3,6,†,‡, Zhuojun Zhou(周卓俊)2, and Le Luo(罗乐)1,4,5   

  1. 1 School of Physics and Astronomy, Sun Yat-Sen University, Zhuhai 519082, China;
    2 QUDOOR Co., Ltd., Hefei 230000, China;
    3 QUDOOR Co., Ltd., Beijing 100089, China;
    4 Shenzhen Research Institute of Sun Yat-Sen University, Shenzhen 518057, China;
    5 Guangdong Provincial Key Laboratory of Quantum Metrology and Sensing, Sun Yat-Sen University, Zhuhai 519082, China;
    6 Yangtze Delta Industrial Innovation Center of Quantum Science and Technology, Suzhou 215000, China
  • Received:2024-12-02 Revised:2025-07-14 Accepted:2025-07-28 Published:2025-09-29
  • Contact: Xu Zhou E-mail:zhoux359@mail.sysu.edu.cn
  • Supported by:
    This work was supported in part by the China Postdoctoral Science Foundation (Grant No. 2023M740874).

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摘要： The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum (NISQ) era. The unit commitment (UC) problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost. In this paper, we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network (QNN). This method is tested with a ten-unit system under the power balance constraint. In order to improve computing precision and reduce network complexity, we propose a knowledge-based partially connected quantum neural network (PCQNN). The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.

关键词: quantum computing, quantum algorithm, unit commitment, quantum neural network, noisy intermediate-scale quantum era

Abstract: The quantum hybrid algorithm has recently become a very promising and speedy method for solving larger-scale optimization problems in the noisy intermediate-scale quantum (NISQ) era. The unit commitment (UC) problem is a fundamental problem in the field of power systems that aims to satisfy the power balance constraint with minimal cost. In this paper, we focus on the implementation of the UC solution using exact quantum algorithms based on the quantum neural network (QNN). This method is tested with a ten-unit system under the power balance constraint. In order to improve computing precision and reduce network complexity, we propose a knowledge-based partially connected quantum neural network (PCQNN). The results show that exact solutions can be obtained by the improved algorithm and that the depth of the quantum circuit can be reduced simultaneously.

Key words: quantum computing, quantum algorithm, unit commitment, quantum neural network, noisy intermediate-scale quantum era

中图分类号:  (Quantum algorithms, protocols, and simulations)

  • 03.67.Ac
03.67.Lx (Quantum computation architectures and implementations) 88.50.Mp (Electricity generation, grid integration from wind) 45.10.Db (Variational and optimization methods)