Regularization-Enhanced Hybrid Quantum-Classical Neural Network for Smart Grid Stability Classification
19th IEEE Dallas Circuits and Systems Conference, DCAS 2026, Texas, Amerika Birleşik Devletleri, 10 - 12 Nisan 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/dcas69364.2026.11544419
- Basıldığı Şehir: Texas
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Anahtar Kelimeler: hybrid neural network, Quantum machine learning, smart grid, stability prediction, variational quantum circuit
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Smart grid stability prediction is crucial for providing uninterrupted power and high maintainability while operating smart grids. State-of-the-art research focuses on using Machine Learning (ML) to solve smart grid stability prediction; however, the complex nature of smart grids that serve thousands of users poses challenges for classical ML. Quantum Machine Learning (QML) has emerged as a possible solution to overcome these challenges, making QML a promising approach for energy applications. While QML shows promise in energy research, its application to grid stability problems remains unexplored. In this study, we propose a regularization-enhanced hybrid quantum-classical neural network that combines a classical encoder-decoder architecture with a Variational Quantum Circuit (VQC). Our model achieves 92.25 % accuracy, competitive with parameter-matched classical neural networks while outperforming Gradient Boosting and Random Forest by 3.0-4.25%. Noise robustness analysis shows that the proposed hybrid model demonstrates a slight advantage under dropout noise while maintaining comparable performance under Gaussian and uniform noise, outperforming tree-based methods across all noise types.