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arXiv:2502.13578

Models of liquid-sample confinement for nanoscale NMR

Santiago Oviedo-Casado, Daniel Cohen, Allan Josúe González-Villalobos, Javier Cerrillo

quant-phcond-mat.mes-hallSubmitted Feb 19, 2025• Announced Feb 20, 2025• Revised Nov 2, 2026 (v2)
v1 · Feb 19, 2025v2 · Nov 2, 2026
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Abstract

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.

Journal reference
Phys. Rev. Applied 26, 034018 (2026)
DOI
10.1103/7hbs-1q24
Comments
22 pages and six figures, including appendixes. Comments most welcome

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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.
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.
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.
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.
Nuclear magnetic resonance (NMR) schemes can be applied to micron-, and nanometer-sized samples by the aid of quantum sensors such as nitrogen vacancy (NV) color centers in diamond. These minute devices allow for magnetometry of nuclear spin ensembles with high spatial and frequency resolution at ambient conditions, thus having a clear impact in different areas such as chemistry, biology, medicine, and material sciences. In practice, NV quantum sensors are driven by microwave (MW) control fields with a twofold objective: On the one hand, MW fields bridge the energy gap between NV and nearby nuclei which enables a coherent and selective coupling among them while, on the other hand, MW fields remove environmental noise on the NV leading to enhanced interrogation time. In this work we review distinct MW radiation patterns, or dynamical decoupling techniques, for nanoscale NMR applications.
Recent advances in the field of quantum computing hardware motivate the search for applications which demonstrate so-called quantum advantage. One promising use case that has been identified is the simulation of quantum many-body systems. The computational resources required for performing such a simulation on a classical computer generally grow exponentially with the size of the system being modeled, which is ultimately due to the exponential growth of the Hilbert space in which the dynamics of such a system take place. While digital quantum computers natively evolve quantum states directly in such a Hilbert space, thus naively avoiding this problem, the result of such a computation is typically not obtained as a deterministic output. Rather, it requires projective measurements which are fundamentally affected by shot noise. Any desired expectation values must therefore be reconstructed from repeated measurements, making the number of those measurements a relevant computational resource, and thus an important consideration for any potential claims of quantum advantage. In this work, we study how this resource scales with system size (a scaling which itself depends on the desired accuracy of the final result), using the simulation of nuclear magnetic resonance (NMR) spectra as a test case. We study this scaling for both real-world molecules, as well as a class of model NMR Hamiltonians which allow for efficient large-scale simulations with one-dimensional, two-dimensional, and all-to-all interactions. We find that the required resources increase only weakly with molecular size, far below the exponential growth of the underlying Hilbert space. This result suggests that shot noise should not pose a fundamental barrier to achieving quantum advantage in the simulation of NMR systems, and perhaps for many-body systems more broadly.