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Title: Quantum Machine Learning over Infinite Dimensions

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.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. Ulm Univ. (Germany)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Tennessee, Knoxville, TN (United States)
  4. CipherQ, Toronto, ON (Canada)
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
Physical Review Letters
Additional Journal Information:
Journal Volume: 118; Journal Issue: 8; Journal ID: ISSN 0031-9007
Publisher:
American Physical Society (APS)
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
OSTI Identifier:
1435294
Alternate Identifier(s):
OSTI ID: 1344616

Lau, Hoi-Kwan, Pooser, Raphael, Siopsis, George, and Weedbrook, Christian. Quantum Machine Learning over Infinite Dimensions. United States: N. p., Web. doi:10.1103/PhysRevLett.118.080501.
Lau, Hoi-Kwan, Pooser, Raphael, Siopsis, George, & Weedbrook, Christian. Quantum Machine Learning over Infinite Dimensions. United States. doi:10.1103/PhysRevLett.118.080501.
Lau, Hoi-Kwan, Pooser, Raphael, Siopsis, George, and Weedbrook, Christian. 2017. "Quantum Machine Learning over Infinite Dimensions". United States. doi:10.1103/PhysRevLett.118.080501. https://www.osti.gov/servlets/purl/1435294.
@article{osti_1435294,
title = {Quantum Machine Learning over Infinite Dimensions},
author = {Lau, Hoi-Kwan and Pooser, Raphael and Siopsis, George and Weedbrook, Christian},
abstractNote = {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.},
doi = {10.1103/PhysRevLett.118.080501},
journal = {Physical Review Letters},
number = 8,
volume = 118,
place = {United States},
year = {2017},
month = {2}
}