Quantum kernel learning achieves battery prognostics on noisy quantum hardware

Battery management systems need accurate lifetime predictions to prevent failures and optimize replacement schedules, but acquiring training data requires months of controlled aging experiments per cell. We demonstrate that quantum computers can learn battery degradation patterns from far less data than classical methods. Using quantum kernel regression on 64 lithium-iron-phosphate cells, we achieved 2.1 % better prediction accuracy than classical approaches, but more importantly, required 6.6 times fewer training samples to reach equivalent performance. Real-world validation on IBM's 156-qubit quantum processor revealed severe noise challenges—performance dropped 54 %—but advanced error mitigation recovered 38 % of this loss. The quantum advantage emerges specifically in data-limited regimes where each additional cell requires months of testing. This positions quantum machine learning as a practical accelerator for battery research, materials discovery, and other domains where data acquisition time dominates development cycles.