Fock state-enhanced expressivity of quantum machine learning models

URI	http://purl.tuc.gr/dl/dias/9CB079A3-6AEB-49C4-8B09-FA578377E764	-
Identifier	https://ieeexplore.ieee.org/document/9571235	-
Language	en	-
Extent	2 pages	en
Title	Fock state-enhanced expressivity of quantum machine learning models	en
Creator	Yee Gan Beng	en
Creator	Leykam Daniel	en
Creator	Angelakis Dimitrios	en
Creator	Αγγελακης Δημητριος	el
Publisher	Institute of Electrical and Electronics Engineers	en
Content Summary	We propose quantum classifiers based on encoding classical data onto Fock states using tunable beam-splitter meshes, similar to the boson sampling architecture. We show that higher photon numbers enhance the expressive power of the circuit.	en
Type of Item	Σύντομη Δημοσίευση σε Συνέδριο	el
Type of Item	Conference Short Paper	en
License	http://creativecommons.org/licenses/by/4.0/	en
Date of Item	2023-05-29	-
Date of Publication	2021	-
Subject	Machine learning	en
Subject	Laser modes	en
Subject	Encoding	en
Subject	Integrated circuit modeling	en
Subject	Electrooptical waveguides	en
Subject	Photonics	en
Bibliographic Citation	G. B. Yee, D. Leykam and D. G. Angelakis, "Fock state-enhanced expressivity of quantum machine learning models," presented at the 2021 Conference on Lasers and Electro-Optics (CLEO), San Jose, CA, USA, 2021.	en

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