aiqbench analysis
This study presents a significant advancement in quantum sensing by utilizing machine learning to fully reconstruct spectral density parameters from nitrogen-vacancy center dynamics, surpassing previous methods that only estimated central frequencies. The neural network model achieves performance comparable to maximum likelihood estimators while reliably reproducing system behavior over the observation window. This work bridges AI and quantum physics to optimize environmental characterization for future quantum technologies.
Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre
arXiv:2608.27632v1 Announce Type: new 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 Cram\'er-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.
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.
arXiv:2608.27632 [quant-ph]
(or arXiv:2608.27632v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.27632 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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