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