Modified Quantum Long-Short Term Memory with Variational Quantum Circuits for PV Power Forecasting

• Published in: Conference record of the Industry Applications Conference pp. 1 - 7
• Main Authors: Phan, Ha-Vu, Pham, Tan-Hung, Tran, Khang B., Phan, Quoc-Thang, Phan, Quoc Dung, Wu, Yuan-Kang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.06.2025
• Subjects: Computational modeling; Forecasting; Integrated circuit modeling; Long short term memory; Long Short-Term Memory (LSTM); Memory management; mQLSTM; Photovoltaic (PV) power; Photovoltaic systems; Quantum circuit; Quantum entanglement; Quantum Long Short-Term Memory (QLSTM); Quantum Machine Learning; Renewable Energy; Renewable energy sources; Time series analysis; Time Series Forecasting; Variational Quantum Circuits (VQC)
• ISSN: 2576-702X
• DOI: 10.1109/IAS62731.2025.11061559

Time series forecasting is vital in many fields, including energy demand prediction and financial markets. Long Short-Term Memory (LSTM) networks have demonstrated strong performance in capturing temporal dependencies in sequential data. However, traditional LSTM face challenges such as vanishing gradients and limited capabilities in modeling long-term dependencies, especially with complex, high-dimensional data. The model integrates Variable Quantum Circuit (VQC) into a modified LSTM (mLSTM) structure, taking advantage of quantum properties such as superposition and entanglement to improve memory retention and computational efficiency. We would like to introduce modified Quantum Long Short-Term Memory (mQLSTM), which is specifically designed for photovoltaic (PV) energy forecasting. Key improvements include a quantum memory mechanism that mitigates loss of information over extended sequences, a redesigned forget gate to enhance long-term learning and an Exponentially Weighted Feature (EWF) layer to accelerate convergence and enhance generalization. This paper highlights the potential of quantum-enhanced deep learning architectures to advance time series forecasting and opens new directions for practical applications in renewable energy and beyond.