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arXiv · 1603.06222

Quantum machine learning over infinite dimensions

Hoi-Kwan Lau·Raphael Pooser·George Siopsis·Christian Weedbrook
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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 achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.

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Hoi-Kwan Lau, Raphael Pooser, George Siopsis, Christian Weedbrook. 2016-03-20. Quantum machine learning over infinite dimensions. https://doi.org/10.1103/physrevlett.118.080501

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