Quantum Neural Networks for Power Flow Analysis
It addresses power flow analysis for electrical grid management, but is incremental as it applies existing quantum methods to a new domain.
This paper tackles power flow analysis by applying quantum and hybrid quantum-classical neural networks, finding that the hybrid model outperforms both quantum and classical neural networks in metrics like generalization and robustness.
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.