4.0 Article

Deep learning enhanced noise spectroscopy of a spin qubit environment

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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

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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.

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