Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2305.14881 · Physical Review Letters

Optimal Sensing Protocol for Statistically Polarized Nano-NMR with NV Centers

Nicolas Staudenmaier, Anjusha Vijayakumar-Sreeja, Genko T. Genov, D. Cohen, Christoph Findler, Johannes Lang, Alex Retzker, Fedor Jelezko, Santiago Oviedo-Casado

Published Oct 10, 2023• 12 citations
arXivHTMLPDF

Abstract

Diffusion noise represents a major constraint to successful liquid state nano-NMR spectroscopy. Using the Fisher information as a faithful measure, we theoretically calculate and experimentally show that phase sensitive protocols are superior in most experimental scenarios, as they maximize information extraction from correlations in the sample. We derive the optimal experimental parameters for quantum heterodyne detection (Qdyne) and present the most accurate statistically polarized nano-NMR Qdyne detection experiments to date, leading the way to resolve chemical shifts and J couplings at the nanoscale.

DOI
10.1103/physrevlett.131.150801

Related papers

Efficient detection of statistical RF fields with a quantum sensor

R. Maier, C. Ho, Hitoshi Sumiya +4 more

Nuclear magnetic resonance (NMR) spectroscopy is widely used in fields ranging from chemistry, materials science to neuroscience. Nanoscale NMR spectroscopy using Nitrogen-vacancy (NV) centers in diamond has emerged as a promising platform due to an unprecedented sensitivity down to the single spin level. At the nanoscale, high nuclear spin polarization through spin fluctuations (statistical polarization) far outweighs thermal polarization. However, until now efficient NMR detection using coherent averaging techniques could not be applied to the detection of statistical polarization, leading to long measurement times. Here we present two protocols to enable coherent averaging of statistically oscillating signals through rectification. We demonstrate these protocols on an artificial radiofrequency signal detected with a single NV center at 2.7 T. The signal-to-noise scaling with number of measurements 𝑁 increases from 𝑁0.5 to 𝑁1, improving the measurement time significantly. The relevance of rectification for the detection of statistically polarized nuclear spins using ensembles of NV centers is outlined, paving the way for efficient nanoscale NMR spectroscopy.

•Dec 1, 2025•0 citations

Efficient Detection of Statistical RF Fields with a Quantum Sensor

Rouven Maier, Cheng-I Ho, Hitoshi Sumiya +4 more

Nuclear magnetic resonance (NMR) spectroscopy is widely used in fields ranging from chemistry and materials science to neuroscience. Nanoscale NMR spectroscopy using nitrogen-vacancy (NV) centers in diamond has emerged as a promising platform due to an unprecedented sensitivity down to the single spin level. At the nanoscale, high nuclear spin polarization through spin fluctuations (statistical polarization) far outweighs thermal polarization. However, until now efficient NMR detection using coherent averaging techniques could not be applied to the detection of statistical polarization, leading to long measurement times. Here we present two protocols to enable coherent averaging of stochastic oscillatory signals through rectification. We demonstrate these protocols on an artificial radio frequency signal detected with a single NV center at 2.7 T. The signal-to-noise scaling with number of measurements N increases from N^{0.5} to N^{1}, improving the measurement time significantly. The relevance of rectification for the detection of statistically polarized nuclear spins using ensembles of NV centers is outlined, paving the way for efficient nanoscale NMR spectroscopy.

arXiv:2503.12954•Physical Review Letters•Aug 26, 2025•3 citations

Models of liquid-sample confinement for nanoscale NMR

Santiago Oviedo-Casado, Daniel Cohen, Allan Josúe González-Villalobos +1 more

Diffusion is a prominent source of noise affecting nuclear magnetic resonance at the nanometer scale (nano-NMR), preventing high-resolution studies of unpolarized liquid samples and hindering the development of nanofluid devices. Actively managing diffusion noise through sample confinement is likely to unveil alternative noise sources that have so far been disregarded, as they occur in longer timescales and consequently are masked by diffusion. These secondary noise sources could diminish the advantages provided by sample confinement, but they may also yield valuable information about the behavior of the fluid sample and its interactions with the confinement device. In this article, we study in detail two previously unaccounted noise models for confined nano-NMR, namely, surface interactions and porosity, and discuss their implications for typical nano-NMR experiments.

quant-pharXiv:2502.13578•Feb 20, 2025

Quantum Memory Enhanced Multipoint Correlation Spectroscopy for Statistically Polarized NMR

Tobias Spohn, Nicolas Staudenmaier, Philipp J. Vetter +6 more

Nuclear magnetic resonance spectroscopy with solid-state spin sensors is a promising pathway for the detection of nuclear spins at the micro- and nanoscale. Although many nanoscale experiments rely on a single sensor spin for the detection of the signal, leveraging spin ensembles can enhance sensitivity, particularly in cases in which the signal merely originates from statistically polarized nuclear spins. In this Letter, we introduce multipoint correlation spectroscopy, which combines the advantages of two well-established methods-correlation spectroscopy and quantum heterodyne detection-to enable temporally efficient measurements of statistically polarized samples at the nanoscale with spin ensembles. We present a theoretical framework for this approach and demonstrate an experimental proof of concept with a nitrogen vacancy center in diamond. We achieve single hertz uncertainty in the estimated signal frequency, highlighting the potential applications of the technique for nanoscale nuclear magnetic resonance.

arXiv:2503.18930•Physical Review Letters•Nov 19, 2025•2 citations

Quantum Sensing via Large Spin-Clusters in Solid-State NMR: Optimal coherence order for practical sensing

Conan Alexander, T S Mahesh

Quantum entanglement has long been recognized as an important resource for quantum sensing. In this work, we demonstrate the use of multiple-quantum solid-state NMR for quantum sensing by creating, manipulating, and detecting large clusters of correlated nuclear spins. We show that such clusters can sensitively detect pulse-width jitters in radio-frequency control fields at the level of tens of nanoseconds. By analyzing the response of high-order quantum coherences to these control-field jitters, we investigate the critical interplay between the enhanced sensitivity offered by large coherence orders, their relative distributions, and their varying susceptibility to decoherence. We further demonstrate that, even within a non-uniform distribution of coherence orders, there exists an optimal maximum coherence order that maximizes sensing efficiency. To support our interpretation, we supplement the experimental results with a simplified numerical model that estimates the corresponding quantum Fisher information. These results support the solid-state NMR platform as a valuable testbed for investigating many-body quantum metrology protocols.

quant-pharXiv:2512.00494•Physical Review A•Dec 2, 2025•0 citations

NV center based nano-NMR enhanced by deep learning

N. Aharon, Amit Rotem, Liam Paul McGuinness +3 more

The growing field of nano nuclear magnetic resonance (nano-NMR) seeks to estimate spectra or discriminate between spectra of minuscule amounts of complex molecules. While this field holds great promise, nano-NMR experiments suffer from detrimental inherent noise. This strong noise masks to the weak signal and results in a very low signal-to-noise ratio. Moreover, the noise model is usually complex and unknown, which renders the data processing of the measurement results very complicated. Hence, spectra discrimination is hard to achieve and in particular, it is difficult to reach the optimal discrimination. In this work we present strong indications that this difficulty can be overcome by deep learning (DL) algorithms. The DL algorithms can mitigate the adversarial effects of the noise efficiently by effectively learning the noise model. We show that in the case of frequency discrimination DL algorithms reach the optimal discrimination without having any pre-knowledge of the physical model. Moreover, the DL discrimination scheme outperform Bayesian methods when verified on noisy experimental data obtained by a single Nitrogen-Vacancy (NV) center. In the case of frequency resolution we show that this approach outperforms Bayesian methods even when the latter have full pre-knowledge of the noise model and the former has none. These DL algorithms also emerge as much more efficient in terms of computational resources and run times. Since in many real-world scenarios the noise is complex and difficult to model, we argue that DL is likely to become a dominant tool in the field.

•Nov 28, 2019•21 citations
Diffusion noise represents a major constraint to successful liquid state nano-NMR spectroscopy. Using the Fisher information as a faithful measure, we theoretically calculate and experimentally show that phase sensitive protocols are superior in most experimental scenarios, as they maximize information extraction from correlations in the sample. We derive the optimal experimental parameters for quantum heterodyne detection (Qdyne) and present the most accurate statistically polarized nano-NMR Qdyne detection experiments to date, leading the way to resolve chemical shifts and J couplings at the nanoscale.
Nuclear magnetic resonance (NMR) spectroscopy is widely used in fields ranging from chemistry, materials science to neuroscience. Nanoscale NMR spectroscopy using Nitrogen-vacancy (NV) centers in diamond has emerged as a promising platform due to an unprecedented sensitivity down to the single spin level. At the nanoscale, high nuclear spin polarization through spin fluctuations (statistical polarization) far outweighs thermal polarization. However, until now efficient NMR detection using coherent averaging techniques could not be applied to the detection of statistical polarization, leading to long measurement times. Here we present two protocols to enable coherent averaging of statistically oscillating signals through rectification. We demonstrate these protocols on an artificial radiofrequency signal detected with a single NV center at 2.7 T. The signal-to-noise scaling with number of measurements 𝑁 increases from 𝑁0.5 to 𝑁1, improving the measurement time significantly. The relevance of rectification for the detection of statistically polarized nuclear spins using ensembles of NV centers is outlined, paving the way for efficient nanoscale NMR spectroscopy.
Nuclear magnetic resonance (NMR) spectroscopy is widely used in fields ranging from chemistry and materials science to neuroscience. Nanoscale NMR spectroscopy using nitrogen-vacancy (NV) centers in diamond has emerged as a promising platform due to an unprecedented sensitivity down to the single spin level. At the nanoscale, high nuclear spin polarization through spin fluctuations (statistical polarization) far outweighs thermal polarization. However, until now efficient NMR detection using coherent averaging techniques could not be applied to the detection of statistical polarization, leading to long measurement times. Here we present two protocols to enable coherent averaging of stochastic oscillatory signals through rectification. We demonstrate these protocols on an artificial radio frequency signal detected with a single NV center at 2.7 T. The signal-to-noise scaling with number of measurements N increases from N^{0.5} to N^{1}, improving the measurement time significantly. The relevance of rectification for the detection of statistically polarized nuclear spins using ensembles of NV centers is outlined, paving the way for efficient nanoscale NMR spectroscopy.
Diffusion is a prominent source of noise affecting nuclear magnetic resonance at the nanometer scale (nano-NMR), preventing high-resolution studies of unpolarized liquid samples and hindering the development of nanofluid devices. Actively managing diffusion noise through sample confinement is likely to unveil alternative noise sources that have so far been disregarded, as they occur in longer timescales and consequently are masked by diffusion. These secondary noise sources could diminish the advantages provided by sample confinement, but they may also yield valuable information about the behavior of the fluid sample and its interactions with the confinement device. In this article, we study in detail two previously unaccounted noise models for confined nano-NMR, namely, surface interactions and porosity, and discuss their implications for typical nano-NMR experiments.
Nuclear magnetic resonance spectroscopy with solid-state spin sensors is a promising pathway for the detection of nuclear spins at the micro- and nanoscale. Although many nanoscale experiments rely on a single sensor spin for the detection of the signal, leveraging spin ensembles can enhance sensitivity, particularly in cases in which the signal merely originates from statistically polarized nuclear spins. In this Letter, we introduce multipoint correlation spectroscopy, which combines the advantages of two well-established methods-correlation spectroscopy and quantum heterodyne detection-to enable temporally efficient measurements of statistically polarized samples at the nanoscale with spin ensembles. We present a theoretical framework for this approach and demonstrate an experimental proof of concept with a nitrogen vacancy center in diamond. We achieve single hertz uncertainty in the estimated signal frequency, highlighting the potential applications of the technique for nanoscale nuclear magnetic resonance.
Quantum entanglement has long been recognized as an important resource for quantum sensing. In this work, we demonstrate the use of multiple-quantum solid-state NMR for quantum sensing by creating, manipulating, and detecting large clusters of correlated nuclear spins. We show that such clusters can sensitively detect pulse-width jitters in radio-frequency control fields at the level of tens of nanoseconds. By analyzing the response of high-order quantum coherences to these control-field jitters, we investigate the critical interplay between the enhanced sensitivity offered by large coherence orders, their relative distributions, and their varying susceptibility to decoherence. We further demonstrate that, even within a non-uniform distribution of coherence orders, there exists an optimal maximum coherence order that maximizes sensing efficiency. To support our interpretation, we supplement the experimental results with a simplified numerical model that estimates the corresponding quantum Fisher information. These results support the solid-state NMR platform as a valuable testbed for investigating many-body quantum metrology protocols.
The growing field of nano nuclear magnetic resonance (nano-NMR) seeks to estimate spectra or discriminate between spectra of minuscule amounts of complex molecules. While this field holds great promise, nano-NMR experiments suffer from detrimental inherent noise. This strong noise masks to the weak signal and results in a very low signal-to-noise ratio. Moreover, the noise model is usually complex and unknown, which renders the data processing of the measurement results very complicated. Hence, spectra discrimination is hard to achieve and in particular, it is difficult to reach the optimal discrimination. In this work we present strong indications that this difficulty can be overcome by deep learning (DL) algorithms. The DL algorithms can mitigate the adversarial effects of the noise efficiently by effectively learning the noise model. We show that in the case of frequency discrimination DL algorithms reach the optimal discrimination without having any pre-knowledge of the physical model. Moreover, the DL discrimination scheme outperform Bayesian methods when verified on noisy experimental data obtained by a single Nitrogen-Vacancy (NV) center. In the case of frequency resolution we show that this approach outperforms Bayesian methods even when the latter have full pre-knowledge of the noise model and the former has none. These DL algorithms also emerge as much more efficient in terms of computational resources and run times. Since in many real-world scenarios the noise is complex and difficult to model, we argue that DL is likely to become a dominant tool in the field.