Frontiers in Optics

Quantum sensing of magnetic fields using quantum hamiltonian learning

Raffaele Santagati, Antonio A. Gentile, Sebastian Knauer, Simon Schmitt, Stefano Paesani, Christopher Granade, Nathan Wiebe, Christian Osterkamp, Liam Paul McGuinness, Jianwei Wang, Mark G. Thompson, John Rarity, Fedor Jelezko, Anthony Laing

Published Jan 1, 2020• 0 citations

Publisher

Abstract

We present our recent results in applying machine learning to the estimation of magnetic fields with NV centers quantum sensors. We report room temperature sensitivities comparable to those so far achieved with cryogenic set-ups.

DOI: 10.1364/fio.2020.ftu8d.3

Related papers

Hamiltonian learning for the estimation of magnetic fields with nanoscale quantum sensors

Raffaele Santagati, Andrea A. Gentile, Sebastian Knauer +11 more

Hamiltonian learning can be used to efficiently characterise quantum systems. Here we apply it to the estimation of magnetic fields with quantum sensors, achieving experimentally, room temperature sensing performance comparable to those of cryogenic set-ups.

Quantum Information and Measurement (QIM)Jan 1, 20190 citations

Magnetic-Field Learning Using a Single Electronic Spin in Diamond with One-Photon Readout at Room Temperature

Raffaele Santagati, A. A. Gentile, Sebastian Knauer +11 more

Machine learning enables a nitrogen-vacancy center to track a changing magnetic field with unprecedented sensitivity at room temperature, paving the way for new and practical nanoscale quantum sensing applications.

arXiv:1807.09753Physical Review XApr 29, 201947 citations

Room Temperature Magnetic Field Learning with Optically Readout Single NV-Centers

Antonio A. Gentile, Jianwei Wang, Mark G. Thompson +11 more

We propose and test a Magnetic Field Learning (MFL) protocol for high-resolution, high dynamic range and high-sensitivity magnetometry with a single NV-center electron spin. Our approach leverages recent proposals that analyze the benefits of adopting classical machine learning to post-process quantum data in quantum sensing protocols [1]. MFL was tested at room-temperature using a setup detecting state-dependent fluorescence via confocal microscopy, and a microwave controlled NV defect in bulk diamond as a sensor [2]. This setup senses the intensity of a magnetic field B in the proximity of the quantum sensor via Ramsey interferometry [3]. In our protocol, the Ramsey precession time τ is adaptively chosen at each step via an efficient particle guess heuristic [1].

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)Jun 1, 20190 citations

QML for Quantum Sensing under Measurement-Induced Information Loss

Sounak Bhowmik, Himanshu Thapliyal

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.

quant-pharXiv:2608.23934Aug 26, 20260 citations

Machine-learning based high-bandwidth magnetic sensing

Galya Haim, Stefano Martina, John Anthony Howell +2 more

Abstract Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-spatial-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.

arXiv:2409.12820Machine Learning Science and TechnologyJun 16, 20253 citations

Nanoscale magnetic imaging with quantum sensors

Aurore Finco

Quantum sensors harness the high sensitivity of quantum systems to external perturbations to accurately measure various physical quantities. Among the quantum systems employed for sensing purposes, the nitrogen-vacancy (NV) defect in diamond has garnered considerable attention as a magnetometer. A single NV center can be integrated in a scanning probe microscope to build a highly sensitive imaging tool which is particularly adapted to the investigation of antiferromagnets. Two approaches are possible, either the measurement of small static stray fields or the detection of magnetic noise.For canted antiferromagnetic textures, like the cycloidal state in multiferroic bismuth ferrite, the NV center can probe the stray field produced by the uncompensated moments, revealing the presence of topological defects in the magnetic order at the surface of bulk single crystals [1].Alternatively, thermally activated spin waves confined in domain walls generate a magnetic noise to which the NV center is also sensitive. This property allows the localization of the domain walls, which was demonstrated in synthetic antiferromagnets [2].Besides the NV centers, new sensors are emerging as magnetic field probes like the boron vacancies in h-BN, which offer the possibilty to integrate a sensing layer inside a van der Waals heterostructure [3].[1] Finco et al., Physical Review Letters, 128, 187201 (2022)[2] Finco et al., Nature Communications 12, 767 (2021)[3] Kumar et al., Physical Review Applied, 18, L061002 (2022)

Mar 22, 20230 citations