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QML for Quantum Sensing under Measurement-Induced Information Loss

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-08-25T00:47:38.000Z

First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.