Deep learning enhanced noise spectroscopy of a spin qubit environment

Anno: 2023

Autori: Martina S., Hernandez-Gomez S., Gherardini S., Caruso F., Fabbri N.

Affiliazione autori: Univ Firenze, Dipartimento Fis & Astron, I-50019 Sesto Fiorentino, Italy; Univ Firenze, European Lab Nonlinear Spect LENS, I-50019 Sesto Fiorentino, Italy; MIT, Res Lab Elect, Cambridge, MA 02139 USA; Consiglio Nazl Ric CNR INO, Ist Nazl Ott, Area Sci Pk, I-34149 Trieste, Italy; Consiglio Nazl Ric CNR INO, Ist Nazl Ott, I-50019 Sesto Fiorentino, Italy.

Abstract: 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.

Giornale/Rivista: MACHINE LEARNING-SCIENCE AND TECHNOLOGY

Volume: 4 (2)      Da Pagina: 02LT01-1  A: 02LT01-12

Maggiori informazioni: This work was supported by the European Commission’s Horizon Europe Framework Programme under the Research and Innovation Action GA n.’101070546-MUQUABIS, and by the European Defence Agency under the project Q-LAMPS Contract No. B PRJ-RT-989. S H G acknowledges support from CNR-FOE-LENS-2020. S M acknowledges financial support from PNRR MUR project PE0000023-NQSTI. F C also acknowledges the European Union’s Horizon 2020 research and innovation programme under FET-OPEN GA n.’828946-PATHOS.
Parole chiavi: deep learning; neural networks; machine learning; quantum machine learning; quantum noise; quantum sensing; quantum noise spectroscopy
DOI: 10.1088/2632-2153/acd2a6

Citazioni: 6
dati da “WEB OF SCIENCE” (of Thomson Reuters) aggiornati al: 2025-06-15
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