# Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

arXiv: https://arxiv.org/abs/2104.04855 · HTML: https://mlsanity.com/paper/2104.04855/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Intelligent Time-Adaptive Transient Stability Assessment System](https://mlsanity.com/paper/1610.08235/) (2016)
- [Asymmetry underlies stability in power grids](https://mlsanity.com/paper/2103.10952/) (2021)
- [Hierarchical Plug-and-Play Voltage/Current Controller of DC Microgrid Clusters with Grid-Forming/Feeding Converters: Line-independent Primary Stabilization and Leader-based Distributed Secondary Regulation](https://mlsanity.com/paper/1707.07259/) (2017)
- [Consider ethical and social challenges in smart grid research](https://mlsanity.com/paper/1912.00783/) (2019)

## Built on by (most cited first, 7 of 7)

- [Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations](https://mlsanity.com/paper/2506.14795/) (2025)
- [A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation](https://mlsanity.com/paper/2302.02547/) (2023)
- [Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression](https://mlsanity.com/paper/2412.18571/) (2024)
- [Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies](https://mlsanity.com/paper/2506.19973/) (2025)
- [Quantum Neural Networks for Solving Power System Transient Simulation Problem](https://mlsanity.com/paper/2405.11427/) (2024)
- [QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks](https://mlsanity.com/paper/2512.09936/) (2025)
- [Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems](https://mlsanity.com/paper/2601.00873/) (2025)

## Abstract (arXiv)

Transient stability assessment (TSA) is a cornerstone for resilient operations of today's interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA challenge. We devise a quantum TSA (qTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A low-depth, high expressibility quantum neural network for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm for efficient qTSA training; 3) A systematical analysis on qTSA's performance under various quantum factors. qTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of qTSA.