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RESEARCH PRODUCT
Quantum pattern recognition in photonic circuits
F. Albarrán-arriagadaEnrique SolanoRui WangCarlos Hernani-moralesJosé D. Martín-guerrerosubject
FOS: Computer and information sciencesQuantum PhysicsComputer Science - Machine LearningData processingPhotonCondensed Matter - Mesoscale and Nanoscale PhysicsPhysics and Astronomy (miscellaneous)business.industryComputer scienceMaterials Science (miscellaneous)FOS: Physical sciencesQuantum entanglementAtomic and Molecular Physics and OpticsMachine Learning (cs.LG)Pattern recognition (psychology)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)Coherent statesElectrical and Electronic EngineeringPhotonicsbusinessQuantum Physics (quant-ph)AlgorithmQuantumElectronic circuitdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 | Quantum Science and Technology |