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.
- Title
- Fock State-enhanced Expressivity of Quantum Machine Learning Models
- Creators - without role
- Gan Beng Yee - National University of Singapore,Centre for Quantum Technologies,Singapore,117543Daniel Leykam - Duke-NUS Medical SchoolDimitris G. Angelakis - Duke-NUS Medical SchoolIEEE
- Publication Details
- 2021 Conference on Lasers and Electro-Optics (CLEO), pp.1-2
- Publisher
- OSA
- Number of pages
- 2
- Identifiers
- 9911502209846
- Academic Unit
- SCI,MATH & Technology
- Language
- English
- Resource Type
- Conference proceeding