# Deep learning enhanced noise spectroscopy of a spin qubit environment

arXiv: https://arxiv.org/abs/2301.05079 · HTML: https://mlsanity.com/paper/2301.05079/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 89 references that are in the map.
- First posted on arXiv 12 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Learning the noise fingerprint of quantum devices](https://mlsanity.com/paper/2109.11405/) (2021)
- [Noise fingerprints in quantum computers: Machine learning software tools](https://mlsanity.com/paper/2202.04581/) (2022)

## Built on by (most cited first, 6 of 6)

- [Quantum-Noise-Driven Generative Diffusion Models](https://mlsanity.com/paper/2308.12013/) (2023)
- [The role of data embedding in equivariant quantum convolutional neural networks](https://mlsanity.com/paper/2312.13250/) (2023)
- [Machine-learning based noise characterization and correction on neutral atoms NISQ devices](https://mlsanity.com/paper/2306.15628/) (2023)
- [Quantum Feature Space of a Qubit Coupled to an Arbitrary Bath](https://mlsanity.com/paper/2505.03397/) (2025)
- [Machine-learning based high-bandwidth magnetic sensing](https://mlsanity.com/paper/2409.12820/) (2024)
- [Quantum Diffusion Models for Medical Image Analysis](https://mlsanity.com/paper/2609.31070/) (2026)

## Abstract (arXiv)

The undesired interaction of a quantum system with its environment generally leads to a coherence decay of superposition states in time. A precise knowledge of the spectral content of the noise induced by the environment is crucial to protect qubit coherence and optimize its employment in quantum device applications. We experimentally show that the use of neural networks can highly increase the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy (NV) center in diamond. Neural networks are trained over spin coherence functions of the NV center subjected to different Carr-Purcell sequences, typically used for dynamical decoupling (DD). As a result, we determine that deep learning models can be more accurate than standard DD noise-spectroscopy techniques, by requiring at the same time a much smaller number of DD sequences.