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 periodAdditional details
Identifiers
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