Sakthi Prabhu Gunasekar

Sakthi Prabhu Gunasekar, Prasanna Kumar Rangarajan

Battery health diagnostics today rely overwhelmingly on electrochemical signals measured at the cell terminals. A parallel literature has shown that magnetic sensing can resolve information that terminal-only measurements miss, but method development is limited by the absence, to the best of our knowledge, of public battery magnetic-measurement datasets paired with degradation labels. We release MagBridge-Battery v1.0, a synthetic dataset of 6,760 magnetic-field signatures that bridges real magnetic morphology from the Mohammadi-Jerschow Open Science Framework (OSF) archive with state-of-health (SOH) labels from the PulseBat dataset. The release contains 5,600 PulseBat-conditioned grounded samples, 600 synthetic sensor-anomaly samples derived from clean parents, and 560 low-voltage Regime-B extrapolation samples. A cell-disjoint, parent-child-leakage-free primary benchmark split is verified to contain zero overlapping cells, zero cross-split parent-child pairs, and zero sample-ID overlap. We define three primary benchmark tasks: SOH regression, second-life classification, and anomaly detection, plus an auxiliary anomaly-subtype classification task. A controlled label-shuffle ablation collapses SOH regression from R^2 approximately 0.77 to approximately 0, confirming that the bridge encodes input SOH non-trivially rather than producing label-aligned artifacts. The dataset is released on Zenodo under CC-BY-4.0, and the bridge code and benchmark suite are released under Apache-2.0. This work provides a public benchmark for magnetic-sensing battery diagnostics while paired magnetic-electrochemical measurements remain scarce.

Sakthi Prabhu Gunasekar, Prasanna Kumar R

Synthetic Aperture Radar (SAR) data is inherently complex-valued, while quantum machine learning (QML) models naturally operate in complex Hilbert spaces. This apparent alignment suggests that incorporating both magnitude and phase information into quantum encoding should improve performance in SAR Automatic Target Recognition (ATR). In this work, we systematically evaluate this assumption by comparing five quantum encoding strategies: magnitude-only, joint complex, I/Q-based, preprocessed phase, and pure quantum, under a unified experimental framework on the MSTAR benchmark dataset. Contrary to expectation, we observe a consistent pattern: in hybrid quantum-classical architectures, magnitude-only encoding outperforms all complex-valued strategies, achieving 99.57% accuracy on a 3-class task and 71.19% on an 8-class task, while phase-aware methods provide negligible (~0%) or negative improvements. In contrast, in purely quantum architectures with only 184-224 trainable parameters and no classical components, phase information becomes essential, contributing up to 21.65% improvement in accuracy. These results reveal that the utility of phase information is not inherent to the data, but depends critically on the model architecture. Hybrid models rely on classical components that compensate for missing phase information, whereas purely quantum models require phase to construct discriminative representations. Our findings provide practical design guidelines for encoding complex-valued data in QML and highlight the importance of encoding-architecture co-design in the NISQ era.