Bayesian estimation for quantum sensing in the absence of single-shot detection
Hossein T. Dinani, Dominic W. Berry, Raul Gonzalez, Jeronimo R. Maze, Cristian Bonato
备注：
7 pages + 2 pages supplementary, 5 figures, In the updated version we have added updating the probability after every single measurement. Comments are welcome
Quantum information protocols, such as quantum error correction and quantum phase estimation, have been widely used to enhance the performance of quantum sensors. While these protocols have relied on single-shot detection, in most practical applications only an averaged readout is available, as in the case of room-temperature sensing with the electron spin associated with a nitrogen-vacancy center in diamond. Here, we theoretically investigate the application of the quantum phase estimation algorithm for high dynamic-range magnetometry, in the case where single-shot readout is not available. We show that, even in this case, Bayesian estimation provides a natural way to use the available information in an efficient way. We apply Bayesian analysis to achieve an optimized sensing protocol for estimating a time-independent magnetic field with a single electron spin associated to a nitrogen-vacancy center at room temperature and show that this protocol improves the sensitivity over previous protocols by more than a factor of 3. Moreover, we show that an extra enhancement can be achieved by considering the timing information in the detector clicks.
Achieving sub-shot-noise sensing at finite temperatures
我们研究在有限温度下使用量子自旋链来感测磁场，并利用量子相位交叉来改善链参数估计的上界。特别是，我们分析了$ XX $自旋链，并表明该系统的磁灵敏度是由其绝热磁化率，广泛（线性）的自旋数$ N $的规模。接下来，我们介绍了一种迭代前馈协议，该协议积极利用量子相位交叉的特征，以实现磁灵敏度的超广泛缩放。此外，我们提供了实验现实的可观测量饱和的量子束缚。最后，我们还解决了磁传感的海森堡$ XY $自旋链。
Single-shot measurement learning as a self-certifying estimator for quantum-enhanced sensing
Institute for Convergence Research and Education in Advanced Technology, Yonsei University（延世大学先进技术与融合教育研究院）；Department of Quantum Information, Yonsei University（延世大学量子信息系）
Signal Processing Systems Group, Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电子工程系信号处理系统组）；KCLIP lab, Center for Intelligent Information Processing Systems, Department of Engineering, King's College London（伦敦大学金史密斯学院工程系智能信息处理系统中心）
