  This paper explores the potential application of quantum and hybrid
quantum-classical neural networks in power flow analysis. Experiments are
conducted using two datasets based on 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, and (v) training process stability. The results
show that the developed hybrid 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) and fault-tolerant quantum (FTQ) era.
