TY - JOUR TI - Online adaptive quantum characterization of a nuclear spin AU - Joas, Timo AU - Schmitt, Simon AU - Santagati, Raffaele AU - Gentile, Antonio Andrea AU - Bonato, Cristian AU - Laing, Anthony AU - McGuinness, Liam P. AU - Jelezko, Fedor T2 - npj Quantum Information AB - The characterization of quantum systems is both a theoretical and technical challenge. Theoretical because of the exponentially increasing complexity with system size and the fragility of quantum states under observation. Technical because of the requirement to manipulate and read out individual atomic or photonic elements. Adaptive methods can help to overcome these challenges by optimizing the amount of information each measurement provides and reducing the necessary resources. Their implementation, however, requires fast-feedback and complex processing algorithms. Here, we implement online adaptive sensing with single spins and demonstrate close to photon shot noise limited performance with high repetition rate, including experimental overheads. We further use fast feedback to determine the hyperfine coupling of a nuclear spin to the nitrogen-vacancy sensor with a sensitivity of $$445\,{\mathrm{nT}}{\sqrt{\mathrm{Hz}}}^{- 1}$$. Our experiment is a proof of concept that online adaptive techniques can be a versatile tool to enable faster characterization of the spin environment. DA - 2021/04/08/ PY - 2021 DO - 10.1038/s41534-021-00389-z DP - www.nature.com VL - 7 IS - 1 SP - 1 EP - 8 LA - en SN - 2056-6387 UR - https://www.nature.com/articles/s41534-021-00389-z Y2 - 2021/04/10/08:21:36 KW - Cristian Bonato KW - QPL KW - machine learning KW - spintronics ER -