Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre
Abstract
The interaction between a quantum system and its environment can be characterized by the spectral density function: knowing its structure is important for optimizing applications of quantum technologies such as quantum sensing protocols. In this work, we present the first experimental demonstration of a machine learning-based reconstruction of reaction-coordinate spectral density parameters from NV centre Rabi dynamics. Unlike the previous work, we recover all spectral density parameters rather than only the central frequency, and benchmark the performance of the neural network against the Cramér-Rao bound and maximum likelihood estimator. Our results demonstrate that the model predicted by the neural network can reliably reproduce the NV dynamics over the estimation window, and can produce estimates for some parameters with variances comparable to that of maximum likelihood.
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BibTeX
@misc{lannon2026machine,
title = {{Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre}},
author = {Tom Lannon and Mauro Paternostro and John Martin Gregg and Amit Kumar},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.27632},
eprint = {2608.27632},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.27632},
}
RIS
TY - UNPB
TI - Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre
AU - Tom Lannon
AU - Mauro Paternostro
AU - John Martin Gregg
AU - Amit Kumar
PY - 2026
DA - 2026/08/27
PB - arXiv
DO - 10.48550/arxiv.2608.27632
UR - https://arxiv.org/abs/2608.27632
AB - The interaction between a quantum system and its environment can be characterized by the spectral density function: knowing its structure is important for optimizing applications of quantum technologies such as quantum sensing protocols. In this work, we present the first experimental demonstration of a machine learning-based reconstruction of reaction-coordinate spectral density parameters from NV centre Rabi dynamics. Unlike the previous work, we recover all spectral density parameters rather than only the central frequency, and benchmark the performance of the neural network against the Cramér-Rao bound and maximum likelihood estimator. Our results demonstrate that the model predicted by the neural network can reliably reproduce the NV dynamics over the estimation window, and can produce estimates for some parameters with variances comparable to that of maximum likelihood.
ER -