Fock State-enhanced Expressivity of Quantum Machine Learning Models

Beng Yee Gan; Daniel Leykam; Dimitris G. Angelakis; Dimitris G. Angelakis

doi:10.1364/CLEO_AT.2021.JW1A.73

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper JW1A.73
•https://doi.org/10.1364/CLEO_AT.2021.JW1A.73

Fock State-enhanced Expressivity of Quantum Machine Learning Models

Beng Yee Gan, Daniel Leykam, and Dimitris G. Angelakis

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Joint Poster Session II (JW1A)

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B. Y. Gan, D. Leykam, and D. G. Angelakis, "Fock State-enhanced Expressivity of Quantum Machine Learning Models," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper JW1A.73.

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Abstract

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.

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