This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two small-size datasets based on the IEEE 4-bus and 33-bus test
systems. A systematic performance comparison is also conducted among quantum,
hybrid quantum-classical, and classical neural networks. The comparison is
based on (i) generalization ability, (ii) robustness, (iii) training dataset
size needed, (iv) training error. (v) training computational time, and (vi)
training process stability. The results show that the developed
quantum-classical neural network outperforms both quantum and classical neural
networks, and hence can improve deep learning-based power flow analysis in the
noisy-intermediate-scale quantum (NISQ) era.

探索量子和混合量子 - 经典神经网络在电力流分析中的潜在应用。使用基于 IEEE 4-bus 和 33-bus 测试系统的两个小型数据集进行实验，并对量子、混合量子 - 经典和经典神经网络进行系统性能比较。结果表明，开发的混合量子 - 经典神经网络优于量子和经典神经网络，因此可以改善基于深度学习的电力流分析在噪声中等规模的量子 (NISQ) 时代。