# Machine-learning based high-bandwidth magnetic sensing

arXiv: https://arxiv.org/abs/2409.12820 · HTML: https://mlsanity.com/paper/2409.12820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations](https://mlsanity.com/paper/1708.02691/) (2017)
- [Quantum-Noise-Driven Generative Diffusion Models](https://mlsanity.com/paper/2308.12013/) (2023)
- [Learning the noise fingerprint of quantum devices](https://mlsanity.com/paper/2109.11405/) (2021)
- [Experimental quantum pattern recognition in IBMQ and diamond NVs](https://mlsanity.com/paper/2205.00561/) (2022)
- [Machine learning classification of non-Markovian noise disturbing quantum dynamics](https://mlsanity.com/paper/2101.03221/) (2021)
- [Deep learning enhanced noise spectroscopy of a spin qubit environment](https://mlsanity.com/paper/2301.05079/) (2023)
- [Machine-learning-enhanced quantum sensors for accurate magnetic field imaging](https://mlsanity.com/paper/2202.00380/) (2022)
- [Machine-learning based noise characterization and correction on neutral atoms NISQ devices](https://mlsanity.com/paper/2306.15628/) (2023)
- [Transfer learning with generative models for object detection on limited datasets](https://mlsanity.com/paper/2402.06784/) (2024)
- [Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum Diamond Microscope Magnetic Field Images](https://mlsanity.com/paper/2204.14228/) (2022)

## Built on by (most cited first, 1 of 1)

- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

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.