# Quantum Neural Networks for Solving Power System Transient Simulation Problem

arXiv: https://arxiv.org/abs/2405.11427 · HTML: https://mlsanity.com/paper/2405.11427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 19 May 2024.

## Builds on (most cited first, 13 of 13)

- [On the Convergence of Adam and Beyond](https://mlsanity.com/paper/1904.09237/) (2019)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Layerwise learning for quantum neural networks](https://mlsanity.com/paper/2006.14904/) (2020)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks](https://mlsanity.com/paper/2109.04304/) (2021)
- [An Empirical Comparison of Optimizers for Quantum Machine Learning with SPSA-based Gradients](https://mlsanity.com/paper/2305.00224/) (2023)
- [Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems](https://mlsanity.com/paper/2104.04855/) (2021)
- [Quantum Power Flow](https://mlsanity.com/paper/2104.04888/) (2021)
- [Solving Power System Differential Algebraic Equations Using Differential Transformation](https://mlsanity.com/paper/1903.00935/) (2019)
- [Power System Differential-Algebraic Equations](https://mlsanity.com/paper/1512.05185/) (2015)
- [Expressibility-Enhancing Strategies for Quantum Neural Networks](https://mlsanity.com/paper/2211.12670/) (2022)

## Abstract (arXiv)

Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential algebraic equations (DAEs). We introduce two novel Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the Polynomial-Friendly QNN, proposing them as effective alternatives to conventional simulation techniques. Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy. We further explore various configurations, including time intervals, training points, and the selection of classical optimizers, to optimize the solving of DAEs using QNNs. This research not only marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges.