Application of Power Flow problem to an open quantum neural hardware

TL;DR

This paper explores using a dissipative quantum neural network hardware to solve the power flow problem in electrical systems, demonstrating how quantum approaches can address real-world engineering challenges.

Contribution

It introduces a novel application of quantum neural hardware to the power flow problem, analyzing performance variations with network parameters.

Findings

Quantum neural network can solve power flow problems.

Performance depends on network parameter tuning.

Potential for quantum hardware in power system analysis.

Abstract

Significant progress in the construction of physical hardware for quantum computers has necessitated the development of new algorithms or protocols for the application of real-world problems on quantum computers. One of these problems is the power flow problem, which helps us understand the generation, distribution, and consumption of electricity in a system. In this study, the solution of a balanced 4-bus power system supported by the Newton-Raphson method is investigated using a newly developed dissipative quantum neural network hardware. This study presents the findings on how the proposed quantum network can be applied to the relevant problem and how the solution performance varies depending on the network parameters.