title: Quantum Machine Learning over Infinite Dimensions.
abstract: Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.
authors.affiliation: Institute of Theoretical Physics, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany.
authors.forename: Hoi-Kwan
authors.initials: HK
authors.lastname: Lau
authors.affiliation: Quantum Information Science Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.|Department of Physics and Astronomy, The University of Tennessee, Knoxville, Tennessee 37996-1200, USA.
authors.forename: Raphael
authors.initials: R
authors.lastname: Pooser
authors.affiliation: Department of Physics and Astronomy, The University of Tennessee, Knoxville, Tennessee 37996-1200, USA.
authors.forename: George
authors.initials: G
authors.lastname: Siopsis
authors.affiliation: Xanadu, 10 Dundas Street East, Toronto, M5B 2G9, Canada.
authors.forename: Christian
authors.initials: C
authors.lastname: Weedbrook