Deep learning enhanced noise spectroscopy of a spin qubit environment
This work addresses the challenge of protecting qubit coherence in quantum devices, which is crucial for applications like quantum computing, but it is incremental as it applies an existing deep learning method to a specific noise spectroscopy task.
The researchers tackled the problem of accurately characterizing environmental noise in quantum systems by using neural networks to reconstruct the power spectral density of noise from carbon impurities around a nitrogen-vacancy center in diamond, achieving higher accuracy than standard dynamical decoupling techniques while requiring fewer 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 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. Neural networks 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.