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Generalization Performance of Quantum Metric Learning Classifiers
Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding whic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687469/ https://www.ncbi.nlm.nih.gov/pubmed/36358927 http://dx.doi.org/10.3390/biom12111576 |
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author | Kim, Jonathan Bekiranov, Stefan |
author_facet | Kim, Jonathan Bekiranov, Stefan |
author_sort | Kim, Jonathan |
collection | PubMed |
description | Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding which maximally separates data into classes is learned, was able to perfectly separate ant and bee image training data. The separation is achieved with an intrinsically quantum objective function and the overall approach was shown to work naturally as a hybrid classical-quantum computation enabling embedding of high dimensional feature data into a small number of qubits. However, the ability of the trained classifier to predict test sample data was never assessed. We assessed the performance of quantum metric learning on test ants and bees image data as well as breast cancer clinical data. We applied the original approach as well as variants in which we performed principal component analysis (PCA) on the feature data to reduce its dimensionality for quantum embedding, thereby limiting the number of model parameters. If the degree of dimensionality reduction was limited and the number of model parameters was constrained to be far less than the number of training samples, we found that quantum metric learning was able to accurately classify test data. |
format | Online Article Text |
id | pubmed-9687469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96874692022-11-25 Generalization Performance of Quantum Metric Learning Classifiers Kim, Jonathan Bekiranov, Stefan Biomolecules Article Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding which maximally separates data into classes is learned, was able to perfectly separate ant and bee image training data. The separation is achieved with an intrinsically quantum objective function and the overall approach was shown to work naturally as a hybrid classical-quantum computation enabling embedding of high dimensional feature data into a small number of qubits. However, the ability of the trained classifier to predict test sample data was never assessed. We assessed the performance of quantum metric learning on test ants and bees image data as well as breast cancer clinical data. We applied the original approach as well as variants in which we performed principal component analysis (PCA) on the feature data to reduce its dimensionality for quantum embedding, thereby limiting the number of model parameters. If the degree of dimensionality reduction was limited and the number of model parameters was constrained to be far less than the number of training samples, we found that quantum metric learning was able to accurately classify test data. MDPI 2022-10-27 /pmc/articles/PMC9687469/ /pubmed/36358927 http://dx.doi.org/10.3390/biom12111576 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Jonathan Bekiranov, Stefan Generalization Performance of Quantum Metric Learning Classifiers |
title | Generalization Performance of Quantum Metric Learning Classifiers |
title_full | Generalization Performance of Quantum Metric Learning Classifiers |
title_fullStr | Generalization Performance of Quantum Metric Learning Classifiers |
title_full_unstemmed | Generalization Performance of Quantum Metric Learning Classifiers |
title_short | Generalization Performance of Quantum Metric Learning Classifiers |
title_sort | generalization performance of quantum metric learning classifiers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687469/ https://www.ncbi.nlm.nih.gov/pubmed/36358927 http://dx.doi.org/10.3390/biom12111576 |
work_keys_str_mv | AT kimjonathan generalizationperformanceofquantummetriclearningclassifiers AT bekiranovstefan generalizationperformanceofquantummetriclearningclassifiers |