High-throughput characterization of spin-defects in semiconductors using trans-dimensional Bayesian model selection

Detailed knowledge of the local environments of spin-defects in semiconductors, such as NV centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, direct, full characterization of individual defect environments is not scalable, as spin bath measurements are extremely time consuming. In this work, we address the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin-defects from sparse experimental coherence signals, which can be obtained in hours. A key challenge is determining both the number and type of isotopic nuclear spins along with their hyperfine couplings. To address this challenge, we employ a trans-dimensional Bayesian approach that incorporates ab initio data. This approach provides posterior distributions of the numbers and types of nuclear spins present in the sample, along with their atomic positions and hyperfine couplings. These distributions pave the way for high-throughput sample selection for further characterization, which is crucial for advancing widespread use of spin-defects in semiconductors in quantum sensing and information applications.