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.
Machine learning for experimental single shot phase estimation / Polino, Emanuele; Lumino, Alessandro; Syed Rab, Adil; Milani, Giorgio; Spagnolo, Nicolo'; Sciarrino, Fabio; Nathanwiebe,. - (2019). (Intervento presentato al convegno Quantum Information and Measurement (QIM) V: Quantum Technologies, OSA Technical Digest (Optical Society of America, 2019) tenutosi a Rome; Italy) [10.1364/QIM.2019.T5A.41].
Machine learning for experimental single shot phase estimation
Emanuele Polino;Giorgio Milani;Nicolò Spagnolo;Fabio Sciarrino;
2019
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.| File | Dimensione | Formato | |
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