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Conferences > QIM > 2019 > T5A > T5A.41

Machine Learning For Experimental Single Shot Phase Estimation

Emanuele Polino, Alessandro Lumino, Adil Syed Rab, Giorgio Milani, Nicolò Spagnolo, Fabio Sciarrino, and Nathan Wiebe

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Abstract

We implement adaptive machine learning techniques to enhance the sensitivity in the estimation of a relative phase shift between two paths of an interferometer. The estimation is realized through single photons measured shot by shot.

© 2019 The Author(s)

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