Automatic Detection of Nuclear Spins at Arbitrary Magnetic Fields via Signal-to-Image AI Model

Abstract

Quantum sensors leverage matter's quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpretation challenging. This is especially relevant in low magnetic field scenarios, where standard approximations for modeling the system fail. Additionally, NV signals feature a prominent noise component. In this Letter, we present a signal-to-image deep learning model capable of automatically inferring the number of nuclear spins surrounding a NV sensor and the hyperfine couplings between the sensor and the nuclear spins. Our model is trained to operate effectively across various magnetic field scenarios, requires no prior knowledge of the involved nuclei, and is designed to handle noisy signals, leading to fast characterization of nuclear environments in real experimental conditions. With detailed numerical simulations, we test the performance of our model in scenarios involving varying numbers of nuclei, achieving an average error of less than $2\mathrm{kHz}$ in the estimated hyperfine constants.

arXiv comment4 pages, 3 figures + supplemental material

References in corpus (12)

• Silicon Quantum Electronics
• A 10-qubit solid-state spin register with quantum memory up to one minute
• Detection and control of individual nuclear spins using a weakly coupled electron spin
• One-second coherence for a single electron spin coupled to a multi-qubit nuclear-spin environment
• Learning Quantum Systems
• Robust Dynamical Decoupling Sequences for Individual Nuclear Spin Addressing
• Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images
• Soft Quantum Control for Highly Selective Interactions among Joint Quantum Systems
• Deep learning enhanced individual nuclear-spin detection
• Neural-network-based parameter estimation for quantum detection
• A Neural Network Assisted ${}^{171}$Yb${}^{+}$ Quantum Magnetometer
• Algorithmic decomposition for efficient multiple nuclear spin detection in diamond

Cited by in corpus (3)

• Trans-dimensional Hamiltonian model selection and parameter estimation from sparse, noisy data
• Enhanced sensitivity in microscale high-field NMR via nuclear-spin locking with NV centers
• High-throughput spin-bath characterization of spin-defects in semiconductors