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

TL;DR

This paper presents an exact quantum algorithm utilizing a partially connected quantum neural network to solve the unit commitment problem efficiently, achieving precise solutions with reduced circuit depth in a 10-unit system.

Contribution

The paper introduces a novel knowledge-based partially connected quantum neural network for exact quantum solutions to the unit commitment problem, improving precision and reducing circuit complexity.

Findings

Exact solutions achieved for up to 10-unit systems.

Reduced quantum circuit depth with the proposed method.

Improved computational precision over previous approaches.

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.