Machine-learning based high-bandwidth magnetic sensing

arXiv:2409.12820 · quant-ph, cs.AI, cs.LG, physics.app-ph, physics.comp-ph · Submitted 2024-09-19 · Read on arXiv

Listen

Transcript

Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: Today's paper: "Machine-learning based high-bandwidth magnetic sensing".

Mira: The gist Machine learning tools are implemented to enhance Nitrogen Vacancy (NV) magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios,

Kai: First, who's behind it and why it matters.

Paper summary: Mira: So, to summarize what we’ve heard about "Machine-learning based high-bandwidth magnetic sensing" by Haim and Martina, they've used a neural network to tackle the sensitivity versus bandwidth tradeoff in NV magnetic sensing.

Kai: They are using this machine learning tool as a way to enhance the performance of nitrogen vacancy centers in diamond for magnetic field measurements.

Lev: The authors are showing that this ML approach can reduce the data points needed by at least a factor of three while maintaining their original error level one <ref:2409.12820#pg1>.

Mira: This means they’ve shown how machine learning can be applied to sensing applications, promoting more feasible and efficient quantum technologies nine <ref:2409.12820#pg3>.

Kai: It moves the discussion toward adaptive measurement protocols where you can adjust things on the fly based on what the model predicts.

Lev: It suggests that we can get a better trade-off between high sensitivity and high dynamic range when measuring magnetic fields with these quantum systems.

Mira: Overall, this work shows that machine learning is an emerging field in quantum machine learning, and it's already showing promise for analyzing the data from physical devices ten <ref:2409.12820#pg1>.

Conclusion: Kai: So, looking at the title of this paper 'Machine-learning based high-bandwidth magnetic sensing', it sounds like they're trying to solve a real practical problem.

Mira: I think the core idea is taking something that usually has a trade-off—sensitivity versus how fast you can measure things—and using machine learning to get past that limit.

Lev: From what I see, they’re not just looking at it theoretically; they’re talking about how this could actually translate to running on real hardware, which is where things get tricky with noise and errors.

Kai: They implemented a classical feedforward architecture, a kind of neural network, trained to predict the resonance frequencies from the raw ESR data points.

Mira: That's the method; they framed it as a regression problem where the network learns to map input data directly to those eight predicted central frequencies.

Lev: And I’m thinking about how that training happens, because we need to make sure this isn't just a neat simulation trick, but something robust enough for actual quantum sensing experiments.

Kai: The results they report are pretty striking; they claim a reduction in the number of data points needed by at least three times while keeping the error level the same.

Mira: That’s significant because it means you can get high dynamic range information without having to collect an enormous amount of time for every single measurement.

Lev: But I have to ask, does that reduction in data points translate into a real improvement in something like the signal-to-noise ratio when we think about running this on actual noisy hardware?

Kai: The authors do mention that the network can be trained to be insensitive to variations in noise and lineshape if you feed it enough different kinds of training data.

Mira: That’s the assumption they are making—that by exposing the model to a wide variety of noise conditions during training, it learns a more general mapping rather than just memorizing one specific type of signal.

Lev: If that’s true, then maybe this isn't just about making one specific experiment better; maybe it opens up new ways we can design measurement protocols for things that are naturally very noisy.

Kai: It seems like the implication here is that machine learning can make quantum sensing more adaptable, allowing us to tailor our measurements dynamically instead of relying on a fixed setup.

Mira: It shifts the focus from just building bigger and better sensors to building smarter processing layers on top of those sensors.

Lev: So, if this works as advertised, it means we could potentially achieve higher sensitivity or wider dynamic range in real-world scenarios without needing a massive increase in physical measurement time.

Institute of Applied Physics, Hebrew University · School of Physics, The University of Melbourne · Dept. of Physics and Astronomy, University of Florence · European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence · Racah Institute of Physics, Hebrew University · Istituto Nazionale di Ottica del Consiglio Nazionale delle Ricerche (CNR-INO)

quant-ph, cs.AI, cs.LG, physics.app-ph, physics.comp-ph

Submitted: 2024-09-19

Updated: 2025-06-23

Comments: 12 pages including supplementary, 5 figures, 3 supplementary figures

Journal ref: Galya Haim, Stefano Martina, John Howell et al 2025 Mach. Learn.: Sci. Technol. 6 025074

DOI: 10.1088/2632-2153/ade51c

Code: https://github.com/trianam/machineLearningMagneticSensing

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 80/100

The gist: The gist Machine learning tools are implemented to enhance Nitrogen Vacancy (NV) magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios, potentially reducing

Key concepts

Nitrogen-Vacancy (NV) Color Centers: These are defects in diamond that act as highly sensitive quantum sensors. They are used in this research to measure magnetic fields because their spin states are very sensitive to external magnetic environments, allowing for high spatial resolution and sensitivity.
Spin Resonance Magnetic Sensing (ESR): This is the standard technique used to measure magnetic fields with NV centers. It involves scanning a signal across a range of frequencies. The challenge is that covering a wide frequency range requires many data points, which limits how quickly or precisely the measurement can be made.
Multilayer Perceptron (MLP): This is the specific machine learning model used in the study. It's a type of neural network trained using gradient descent to learn complex relationships between input data (ESR points) and desired outputs (resonance frequencies). It acts as a predictive tool to speed up the sensing process.
Sensitivity/Bandwidth Tradeoff: This is the core problem addressed. High sensitivity means measuring small changes accurately, while high bandwidth means measuring a wide range of frequencies quickly. Traditionally, improving one often degrades the other; this paper aims to break this trade-off using ML.

Terminology

Quantum sensing — 2× in this paper · explained in 2 episodes

Summary

The gist

Machine learning tools are implemented to enhance Nitrogen Vacancy (NV) magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios, potentially reducing required data points by at least a factor of 3 while maintaining error levels.

Background and Problem

Nitrogen-Vacancy (NV) color centers in diamond are a leading platform for quantum sensing, notably magnetic sensing [1] in terms of providing versatile, high-sensitivity, and high-spatial resolution capabilities. Current schemes for spin resonance magnetic sensing suffer from a trade-off between sensitivity and bandwidth, specifically in the high-dynamic range regime. The standard approach to quantitatively measure vectorial magnetic fields using NV centers is Electron Spin Resonance (ESR), which involves scanning the signal over frequency space, often referred to as raster scanning. This scan must cover the entire relevant frequency range determined by the desired dynamic range.

Machine Learning Approach

The paper proposes a Machine Learning (ML) algorithm as a promising approach to address this tradeoff. The ML model employed is a classical feedforward architecture, commonly known as Multilayer Perceptron (MLP) [16], trained using gradient descent to minimize a loss function between the prediction and the desired outputs. The task is framed as a regression problem with all inputs and outputs normalized to fall between 0 and 1.

Model Details and Training

The neural network is configured with an input layer corresponding to the ESR data points λi, where the dimension is determined by the number of data points in an ESR spectrum. The output layer always consists of 8 values that predict the central frequencies of the 8 Lorentzians (y′i). The network is trained using the Mean Squared Error (MSE) loss function: L(y, yˆ) = 1/8n Σ Xn i=1 X 8 j=1 (yi,j − yˆi,j) squared. Hyperparameter optimization is alleviated through the use of the Ray Tune platform [21] and Bayesian optimization called Hyperopt [24] to suggest the most probable best combination for hyperparameters.

Performance and Results

The ML model exhibits better results compared to raster scanning with an error reduced by more than 400 kHz, except for the case of full length data. Remarkably, with only 10% of the data points the ML error is still below 1 MHz. The model maintains similar normalized error for 200 data points as it does for 600 data points, which gives a factor 3 on measurement time preserving the measurement error. Furthermore, the network can be insensitive to variations in noise and lineshape with the right training. The network’s performance benefits from including in the training a range of snr, lineshape, and increasing the dataset size.

Conclusion

The machine learning techniques applied to this quantum setting prove to be efficient, achieving an improved tradeoff between high sensitivity and high dynamic range. They can be adaptively applied to measurements to achieve the desired result. E.g., reducing the measurement time by a at least factor 3, while maintaining the same error, improves the sensitivity; alternatively, the sensitivity can remain constant while reducing the measurement time even further. Such capabilities could have significant impact on a broad range of measurement scenarios with large, time-varying signals.

How it works

The NV ground state is initialized to the ms = 0 ground state under green laser excitation.
An external magnetic field lifts the degeneracy between the ms = ±1 energy levels due to Zeeman shift, given by γB∥.
In the presence of a magnetic field not aligned with any orientation, there will be 8 resonance frequencies as shown in Fig. 1d.
The ML model is trained to predict these resonance positions yi in the ESR spectrum by minimizing the MSE loss L(y, yˆ) = 1/8n Σ Xn i=1 X 8 j=1 (yi,j − yˆi,j) squared.
The network is trained using Adam [23], an optimizer that implements gradient descent with adaptive momentum.

Improvements for AI systems

The improved system can achieve high-bandwidth magnetic sensing by reducing required data points by at least a factor of 3, while maintaining the current error level, as demonstrated by training an appropriate neural network on real and simulated data in large dynamic range scenarios.
The AI system can perform accurate resonance position prediction even in the presence of overlapping resonances, as the paper shows the network still outputs 8 values even for overlapping Lorentzians, which is critical for robust spectral interpretation.
The system can exhibit superior scaling with respect to measurement time by maintaining a similar normalized error for 200 data points as it does for 600 data points, which gives a factor 3 on measurement time preserving the measurement error.
The improved system can be adapted to achieve high sensitivity and dynamic range by leveraging the ML model's ability to be insensitive to variations in noise and lineshape with the right training, resulting in a measurement error that is more robust and scales better for fewer data points.
The AI system can autonomously select optimal training strategies, as demonstrated by using platforms like Ray Tune with Bayesian optimization (Hyperopt) to find hyperparameters that yield the best performance across different noise levels and linewidths.

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.