An Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes - Alexander Hentschel

Precise metrology is an important task with applications to measurements of time, displacements, and magnetic field strength. However, the `standard quantum limit' (SQL) restricts achievable precision, beyond which measurement must be treated on a quantum level. Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is complicated and often involves clever guesswork. I present an automated technique, based on machine-learning that replaces guesswork by a logical, fully automatic, programmable routine. I explain our method using the example of interferometric phase estimation. Our algorithm autonomously learns to perform phase estimation based on experimental trial runs, which can be either simulated or performed using a real world experiment. The algorithm does not require prior knowledge about the experiment and is effective even if the interferometric quantum channel is a black box. Our new technique is robust against loss and decoherence. Furthermore, our algorithm learns to account for systematic experimental imperfections and random noise, thereby making time-consuming error modelling and extensive calibration dispensable. We show that our method outperforms the best known adaptive scheme for interferometric phase estimation.