Journal
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
Volume 4, Issue 2, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2632-2153/acd2a6
Keywords
deep learning; neural networks; machine learning; quantum machine learning; quantum noise; quantum sensing; quantum noise spectroscopy
Ask authors/readers for more resources
The use of neural networks can greatly enhance the accuracy of noise spectroscopy, by reconstructing the power spectral density that characterizes an ensemble of carbon impurities around a nitrogen-vacancy center in diamond. Deep learning models can be more accurate than standard noise-spectroscopy techniques, while requiring a smaller number of sequences.
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 (NNs) 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. NNs 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available