Quantum learning without quantum memory
A quantum learning machine for binary classification of qubit states that does not require quantum memory is introduced and shown to perform with the minimum error rate allowed by quantum mechanics for any size of the training set. This result is shown to be robust under (an arbitrary amount of) noi...
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| Format: | Online |
| Language: | English |
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Nature Publishing Group
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3464493/ |
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pubmed-34644932012-10-05 Quantum learning without quantum memory Sentís, G. Calsamiglia, J. Muñoz-Tapia, R. Bagan, E. Article A quantum learning machine for binary classification of qubit states that does not require quantum memory is introduced and shown to perform with the minimum error rate allowed by quantum mechanics for any size of the training set. This result is shown to be robust under (an arbitrary amount of) noise and under (statistical) variations in the composition of the training set, provided it is large enough. This machine can be used an arbitrary number of times without retraining. Its required classical memory grows only logarithmically with the number of training qubits, while its excess risk decreases as the inverse of this number, and twice as fast as the excess risk of an “estimate-and-discriminate” machine, which estimates the states of the training qubits and classifies the data qubit with a discrimination protocol tailored to the obtained estimates. Nature Publishing Group 2012-10-05 /pmc/articles/PMC3464493/ /pubmed/23050092 http://dx.doi.org/10.1038/srep00708 Text en Copyright © 2012, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Sentís, G. Calsamiglia, J. Muñoz-Tapia, R. Bagan, E. |
| spellingShingle |
Sentís, G. Calsamiglia, J. Muñoz-Tapia, R. Bagan, E. Quantum learning without quantum memory |
| author_facet |
Sentís, G. Calsamiglia, J. Muñoz-Tapia, R. Bagan, E. |
| author_sort |
Sentís, G. |
| title |
Quantum learning without quantum memory |
| title_short |
Quantum learning without quantum memory |
| title_full |
Quantum learning without quantum memory |
| title_fullStr |
Quantum learning without quantum memory |
| title_full_unstemmed |
Quantum learning without quantum memory |
| title_sort |
quantum learning without quantum memory |
| description |
A quantum learning machine for binary classification of qubit states that does not require quantum memory is introduced and shown to perform with the minimum error rate allowed by quantum mechanics for any size of the training set. This result is shown to be robust under (an arbitrary amount of) noise and under (statistical) variations in the composition of the training set, provided it is large enough. This machine can be used an arbitrary number of times without retraining. Its required classical memory grows only logarithmically with the number of training qubits, while its excess risk decreases as the inverse of this number, and twice as fast as the excess risk of an “estimate-and-discriminate” machine, which estimates the states of the training qubits and classifies the data qubit with a discrimination protocol tailored to the obtained estimates. |
| publisher |
Nature Publishing Group |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3464493/ |
| _version_ |
1611913835761893376 |