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Fock state-enhanced expressivity of quantum machine learning models

Yee Gan Beng, Leykam Daniel, Angelakis Dimitrios

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URIhttp://purl.tuc.gr/dl/dias/9CB079A3-6AEB-49C4-8B09-FA578377E764-
Identifierhttps://ieeexplore.ieee.org/document/9571235-
Languageen-
Extent2 pagesen
TitleFock state-enhanced expressivity of quantum machine learning modelsen
CreatorYee Gan Bengen
CreatorLeykam Danielen
CreatorAngelakis Dimitriosen
CreatorΑγγελακης Δημητριοςel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryWe 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 ItemΣύντομη Δημοσίευση σε Συνέδριοel
Type of ItemConference Short Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-29-
Date of Publication2021-
SubjectMachine learningen
SubjectLaser modesen
SubjectEncodingen
SubjectIntegrated circuit modelingen
SubjectElectrooptical waveguidesen
SubjectPhotonicsen
Bibliographic CitationG. 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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