Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow

TL;DR

This paper introduces a hybrid classical-quantum deep learning framework for solving the NP-hard AC-OPF problem, improving accuracy, stability, and physical consistency with minimal quantum resources.

Contribution

It presents a novel hybrid quantum-classical neural network with residual connections and a physics-informed module for efficient AC-OPF solutions.

Findings

Achieves superior accuracy and robustness on IEEE test systems.

Enhances training stability with residual quantum circuit structures.

Requires minimal quantum resources for effective performance.

Abstract

Alternative Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and real-time operation but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid classical-quantum deep learning framework for AC-OPF problem, integrating parameterized quantum circuits (PQCs) for feature extraction with classical deep learning for data encoding and decoding. The proposed framework integrates two types of residual connection structures to mitigate the ``barren plateau" problem in quantum circuits, enhancing training stability and convergence. Furthermore, a physics-informed neural network (PINN) module is incorporated to guarantee tolerable constraint violation, improving the physical consistency and reliability of AC-OPF solutions. Experimental evaluations on multiple IEEE test systems demonstrate that the proposed approach achieves superior accuracy, generalization, and robustness to quantum noise while requiring minimal quantum resources.