Journal
QUANTUM SCIENCE AND TECHNOLOGY
Volume 7, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2058-9565/ac3460
Keywords
machine learning; photonics circuits; quantum information; pattern recognition
Funding
- EU Flagship on Quantum Technologies [820505, 820363]
- National Natural Science Foundation of China (NSFC) [12075145]
- Shanghai Government Grant STCSM [2019SHZDZX01-ZX04]
- Spanish Government (MCIU/AEI/FEDER, UE) [PGC2018-095113-B-I00]
- Basque Government [IT986-16]
- EU FET Open Quromorphic Project
- EU EPIQUS Project
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This paper proposes a machine learning method that characterizes photonic states using a simple optical circuit and data processing of photon number distributions. The trained supervised learning algorithms can predict the degree of entanglement in the photonic states and perform full tomography of photon modes.
This paper proposes a machine learning method to characterize photonic states via a simple optical circuit and data processing of photon number distributions, such as photonic patterns. The input states consist of two coherent states used as references and a two-mode unknown state to be studied. We successfully trained supervised learning algorithms that can predict the degree of entanglement in the two-mode state as well as perform the full tomography of one photonic mode, obtaining satisfactory values in the considered regression metrics.
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