Quantum Neural Networks for Power Flow Analysis

Abstract

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.

8 pages, 13 figures

References in corpus (11)

• Quantum Circuit Learning
• Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
• Efficient Learning for Deep Quantum Neural Networks
• The quest for a Quantum Neural Network
• Hybrid quantum neural network for drug response prediction
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Quantum Computing for Power Flow Algorithms: Testing on real Quantum Computers
• Neural Network-based Power Flow Model
• Quantum Computing Solution of DC Power Flow
• A Quantum Computing Approach for the Unit Commitment Problem
• An In-depth Study of Stochastic Backpropagation

Cited by in corpus (1)

• Adaptive Informed Deep Neural Networks for Power Flow Analysis