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.