Deep learning enhanced individual nuclear-spin detection

TL;DR

This paper introduces a deep learning approach for automatic, rapid identification of nuclear spins using NV centers in diamond, significantly advancing quantum sensing and large-scale quantum information processing.

Contribution

It develops neural network algorithms for noise recovery and spectrum analysis, enabling automatic and efficient characterization of complex spin systems.

Findings

Successfully identified 31 nuclear spins around a single NV center

Accurately determined hyperfine interaction parameters

Demonstrated fast, automated spin detection in experimental settings

Abstract

The detection of nuclear spins using individual electron spins has enabled new opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and controlled multi-qubit registers. However, to image more complex samples and to realize larger-scale quantum processors, computerized methods that efficiently and automatically characterize spin systems are required. Here, we realize a deep learning model for automatic identification of nuclear spins using the electron spin of single nitrogen-vacancy (NV) centers in diamond as a sensor. Based on neural network algorithms, we develop noise recovery procedures and training sequences for highly non-linear spectra. We apply these methods to experimentally demonstrate fast identification of 31 nuclear spins around a single NV center and accurately determine the hyperfine parameters. Our methods can be extended to larger spin systems and are applicable to a wide range of electron-nuclear interaction strengths. These results enable efficient imaging of complex spin samples and automatic characterization of large spin-qubit registers.