Quantum machine learning based on continuous variable single-photon states: an elementary foundation for quantum neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Modeling and Simulation,Signal Processing,Theoretical Computer Science,Statistical and Nonlinear Physics,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.1007/s11128-023-04137-4.pdf
Reference46 articles.
1. Killoran, N., Bromley, T.R., Arrazola, J.M., Schuld, M., Quesada, N., Lloyd, S.: Continuous-variable quantum neural networks. Phys. Rev. Res. 1, 033063 (2019)
2. Phillipson, F.: tQANSWER, pp. 51–56 (2020)
3. Perdomo-Ortiz, A., Benedetti, M., Realpe-Gómez, J., Biswas, R.: Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. Quantum Sci. Technol. 3, 030502 (2018)
4. Benedetti, M., Realpe-Gómez, J., Perdomo-Ortiz, A.: Quantum-assisted Helmholtz machines: a quantum-classical deep learning framework for industrial datasets in near-term devices. Quantum Sci. Technol. 3, 034007 (2018)
5. Gottesman, D., Kitaev, A., Preskill, J.: Encoding a qubit in an oscillator. Phys. Rev. A 64, 012310 (2001)
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