QML for Quantum Sensing under Measurement-Induced Information Loss
Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extracting reliable information from noisy, finite-shot, and measurement-limited sensing data remains a considerable challenge. Whereas, quantum machine learning (QML) offers a potential path to improve parameter estimation by learning nonlinear relationships between quantum-sensing data and the underlying physical signal. In this work, we investigate the role of QML in magnetic-field estimation within an NV center-inspired magnetometry setting. We formulated magnetic field sensing as a supervised...
| Authors | Sounak Bhowmik, Himanshu Thapliyal |
|---|---|
| Primary category | quant-ph |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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