Accurately predicting the lifetime of lithium-ion batteries during early charge–discharge cycles remains a significant challenge due to the nonlinear and weakly expressed degradation dynamics in the initial stages of operation. Classical machine learning (ML) models—although effective in pattern recognition—often face limitations in modeling complex correlations within small, high-dimensional datasets. To address these challenges, this study proposes a Hybrid Quantum–Classical Machine Learning (HQML) framework that integrates a Variational Quantum Circuit (VQC) as a quantum feature encoder with a Gradient Boosting Regressor (GBR) as the classical learner. The proposed approach is implemented using the Qiskit Aer simulator on the MIT Battery Degradation Dataset (124 cells, 42 engineered features). By encoding multi-source degradation descriptors (voltage, capacity, temperature, internal resistance) into Hilbert space via amplitude and angle encoding, the HQML model captures intricate nonlinear feature interactions that are inaccessible to conventional kernels. Experimental results demonstrate that the hybrid model achieves an RMSE of 93 cycles and an R² of 0.94, outperforming the best classical baseline (SVM + Wrapper selection, RMSE = 115, R² = 0.90). Furthermore, quantum observables analysis reveals interpretable correlations between entanglement strengths and physical degradation indicators. These results highlight the potential of quantum machine learning as a powerful paradigm for high-fidelity battery prognostics in the early-life regime.
