Deep learning enhanced individual nuclear-spin detection

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.

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• Machine learning on quantum experimental data toward solving quantum many-body problems
• Deep learning enhanced noise spectroscopy of a spin qubit environment
• Machine Learning-Assisted Manipulation and Readout of Molecular Spin Qubits
• Divergent effects of laser irradiation on ensembles of nitrogen-vacancy centers in bulk and nano-diamonds: implications for biosensing
• Real-time adaptive estimation of decoherence timescales for a single qubit
• Antiferromagnetic spin pumping via hyperfine interaction
• Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model
• Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data
• Controlling discrete time crystals via single-site operations in zero-field diamond quantum simulators
• High-throughput spin-bath characterization of spin-defects in semiconductors