Quantum machine learning over infinite dimensions

TL;DR

This paper extends quantum machine learning to infinite-dimensional systems, specifically continuous-variable photonic computers, demonstrating exponential speedups and providing a blueprint for experimental implementation.

Contribution

It introduces quantum machine learning algorithms for infinite-dimensional systems and details an experimental setup for photonic continuous-variable quantum computers.

Findings

Achieves exponential speedup over classical algorithms.

Provides a practical blueprint for photonic quantum machine learning.

Generalizes quantum machine learning to infinite-dimensional systems.

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.