Higgs analysis with quantum classifiers
We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine...
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2021-01-01
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doaj-8b2f55488389469d8b3098e4ce26a2da2021-08-26T09:27:32ZengEDP SciencesEPJ Web of Conferences2100-014X2021-01-012510307010.1051/epjconf/202125103070epjconf_chep2021_03070Higgs analysis with quantum classifiersBelis Vasilis0González-Castillo Samuel1Reissel Christina2Vallecorsa Sofia3Combarro Elías F.4Dissertori Günther5Reiter Florentin6Institute of Particle Physics and Astrophysics, ETH ZürichFaculty of Sciences, University of OviedoInstitute of Particle Physics and Astrophysics, ETH ZürichCERNDepartment of Computer Science, University of OviedoInstitute of Particle Physics and Astrophysics, ETH ZürichInstitute for Quantum Electronics, ETH ZürichWe have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach.https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03070.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Belis Vasilis González-Castillo Samuel Reissel Christina Vallecorsa Sofia Combarro Elías F. Dissertori Günther Reiter Florentin |
spellingShingle |
Belis Vasilis González-Castillo Samuel Reissel Christina Vallecorsa Sofia Combarro Elías F. Dissertori Günther Reiter Florentin Higgs analysis with quantum classifiers EPJ Web of Conferences |
author_facet |
Belis Vasilis González-Castillo Samuel Reissel Christina Vallecorsa Sofia Combarro Elías F. Dissertori Günther Reiter Florentin |
author_sort |
Belis Vasilis |
title |
Higgs analysis with quantum classifiers |
title_short |
Higgs analysis with quantum classifiers |
title_full |
Higgs analysis with quantum classifiers |
title_fullStr |
Higgs analysis with quantum classifiers |
title_full_unstemmed |
Higgs analysis with quantum classifiers |
title_sort |
higgs analysis with quantum classifiers |
publisher |
EDP Sciences |
series |
EPJ Web of Conferences |
issn |
2100-014X |
publishDate |
2021-01-01 |
description |
We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limitations in both simulation hardware and real quantum hardware — we investigated different feature reduction methods. Their impact on the performance of both the classical and quantum models was assessed. We addressed different implementations of two QML models, representative of the two main approaches to supervised quantum machine learning today: a Quantum Support Vector Machine (QSVM), a kernel-based method, and a Variational Quantum Circuit (VQC), a variational approach. |
url |
https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03070.pdf |
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