Published 2017 | Version v1
Journal article

Quantum Machine Learning over Infinite Dimensions

  • 1. Ulm University (Germany)
  • 2. University of Tennessee, Knoxville, TN (United States)
  • 3. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  • 4. CipherQ, Toronto, ON (Canada)

Description

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-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 experi- mental implementation which can be used as a blueprint for future photonic demonstrations.

Availability note (English)

Available from https://www.osti.gov/pages/servlets/purl/1435294; https://www.osti.gov/pages/biblio/1435294; DOE Accepted Manuscript full text, or the publishers Best Available Version will be available free of charge after the embargo period

Additional details

Publishing Information

Journal Title
Physical Review Letters
Journal Volume
118
Journal Issue
8
Journal Page Range
vp.
ISSN
0031-9007

INIS

Country of Publication
United States
Country of Input or Organization
United States
INIS RN
50031511
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
LEARNING; QUANTUM COMPUTERS; QUANTUM INFORMATION
Descriptors DEC
COMPUTERS; INFORMATION

Optional Information

Contract/Grant/Project number
AC05-00OR22725
Funding organization
USDOE (United States)
Secondary number(s)
OSTIID--1435294