Deep learning enhanced noise spectroscopy of a spin qubit environment

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 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.

20 pages, 3 figures

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Cited by in corpus (8)

• Mapping a 50-spin-qubit network through correlated sensing
• Quantum-Noise-Driven Generative Diffusion Models
• Spectral Density Classification For Environment Spectroscopy
• The role of data embedding in equivariant quantum convolutional neural networks
• Machine-learning based noise characterization and correction on neutral atoms NISQ devices
• Noise Classification in Three-Level Quantum Networks by Machine Learning
• Machine-learning based high-bandwidth magnetic sensing
• A machine learning based approach to the identification of spectral densities in quantum open systems