aiqbench analysis
This research demonstrates that quantum machine learning significantly enhances magnetic field sensing in NV centers only when models have direct access to pre-measurement coherent states, bypassing information loss from standard readout procedures. The study establishes a theoretical upper bound proving that model complexity is secondary to the preservation of quantum coherence for optimal parameter estimation in noisy environments. For practitioners, this highlights a critical design requirement: integrating QML pipelines must prioritize maintaining quantum information integrity before classical data extraction occurs.
QML for Quantum Sensing under Measurement-Induced Information Loss
arXiv:2608.23934v1 Announce Type: cross Abstract: 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 regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.
Abstract: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 regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
arXiv:2608.23934 [quant-ph]
(or arXiv:2608.23934v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.23934 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 302 words
Related coverage
A sensor breaks the classical limit by using quantum measurement
Quantum Zeitgeist · Oct 16, 2026
Moving Detectors Reveal Photon Direction Without Losing Quantum Coherence
Quantum Zeitgeist · May 23, 2026
Researchers Identify Conditions for Minimising Errors in Temperature Estimation
Quantum Zeitgeist · Aug 24, 2026
Tunable Exceptional Points for Quantum Sensing in a Spin--Orbit-Angular-Momentum Coupled BEC
arXiv · Sep 1, 2026