• Technical Conference: 

    15 – 20 May 2022

  • Exhibition: 

    17 – 19 May 2022

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Poster

Fock State-Enhanced Expressivity of Quantum Machine Learning Models (JW1A.73)

Presenter: Beng Yee Gan, Centre for Quantum Technologies

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.

Authors:Beng Yee Gan, Centre for Quantum Technologies / Daniel Leykam, Centre for Quantum Technologies / Dimitris Angelakis, Centre for Quantum Technologies


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