# Quantum Neural Networks for Power Flow Analysis

arXiv: https://arxiv.org/abs/2311.06293 · HTML: https://mlsanity.com/paper/2311.06293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Hybrid quantum neural network for drug response prediction](https://mlsanity.com/paper/2211.05777/) (2022)
- [Solutions of DC OPF are Never AC Feasible](https://mlsanity.com/paper/1912.00319/) (2019)
- [Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation](https://mlsanity.com/paper/2211.08737/) (2022)
- [Neural Network-based Power Flow Model](https://mlsanity.com/paper/2112.08418/) (2021)
- [Quantum Computing for Power Flow Algorithms: Testing on real Quantum Computers](https://mlsanity.com/paper/2204.14028/) (2022)
- [Quantum Power Flow](https://mlsanity.com/paper/2104.04888/) (2021)
- [An In-depth Study of Stochastic Backpropagation](https://mlsanity.com/paper/2210.00129/) (2022)
- [Noise-Resilient Quantum Power Flow](https://mlsanity.com/paper/2211.10555/) (2022)
- [A Quantum Computing Approach for the Unit Commitment Problem](https://mlsanity.com/paper/2212.06480/) (2022)

## Built on by (most cited first, 1 of 1)

- [Adaptive Informed Deep Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2412.02659/) (2024)

## Abstract (arXiv)

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.