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