Circuit-optimized quantum kernels for battery degradation prediction across temperature regimes

Accurate prediction of lithium-ion battery capacity fade across temperature extremes is critical for energy storage reliability, yet classical machine learning methods face challenges with the small, nonlinear datasets typical of accelerated aging studies. Quantum machine learning offers a potential solution through enhanced feature representation, but demonstrations on real quantum hardware remain limited. Here we benchmark quantum kernel ridge regression (Q-KRR) against classical baselines for battery capacity-fade prediction—a representative small-data problem with nonlinear, physics-driven features. Using six physics-informed descriptors, Q-KRR on a noiseless simulator achieved R 2 = 0.739, an 8.7% relative improvement over classical kernel ridge regression (R 2 = 0.680). On IBM's 127-qubit superconducting processor, systematic circuit optimization and targeted readout mitigation yielded R 2 = 0.770, exceeding both the classical baseline (13% relative gain) and the ideal simulator performance (4% absolute gain). This hardware advantage resulted from reducing circuit depth and qubit count while applying selective error mitigation matched to the dominant noise sources. The results demonstrate that quantum kernels can outperform classical methods on current noisy hardware when algorithms are co-designed with device constraints, offering a practical pathway for quantum-enhanced prediction in materials science and engineering domains characterized by limited training data and structured feature spaces.