Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models
The paradigm of Quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the computer as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurate...
- Autores:
-
Payares, Esteban
Martínez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/10620
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/10620
- Palabra clave:
- Parallel Quantum
Computation Approach
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| title |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| spellingShingle |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models Parallel Quantum Computation Approach |
| title_short |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| title_full |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| title_fullStr |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| title_full_unstemmed |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| title_sort |
Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models |
| dc.creator.fl_str_mv |
Payares, Esteban Martínez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Payares, Esteban Martínez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Parallel Quantum Computation Approach |
| topic |
Parallel Quantum Computation Approach |
| description |
The paradigm of Quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the computer as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurately. We suggest experimentation of this potential through an architecture of different quantum models computed in parallel. In this work, we present encouraging results of how it is possible to use Quantum Processing Units analogically to Graphics Processing Units to accelerate algorithms and improve the performance of machine learning models through three experiments. The first experiment was a reproduction of a parity function, allowing us to see how the convergence of a given Quantum model is influenced significantly by computing it in parallel. For the second and third experiments, we implemented an image classification problem by training quantum neural networks and using pre-trained models to compare their performances with the same experiments carried out with parallel quantum computations. We obtained very similar results in the accuracies, which were close to 100% and significantly improved the execution time, approximately 15 times faster in the best-case scenario. We also propose an alternative as a proof of concept to address emotion recognition problems using optimization algorithms and how execution times can be positively affected by parallel quantum computation. To do this, we use tools such as the cross-platform software library PennyLane and Amazon Web Services to access high-end simulators with Amazon Braket and IBM quantum experience. |
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2021 |
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2021-12-10 |
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2022-03-14T20:59:01Z |
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2022-03-14T20:59:01Z |
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2022-03-11 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Payares, Esteban & Martinez Santos, Juan Carlos. (2021). Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models. Journal of Physics: Conference Series. 2090. 012171. 10.1088/1742-6596/2090/1/012171. |
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https://hdl.handle.net/20.500.12585/10620 |
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10.1088/1742-6596/2090/1/012171 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Payares, Esteban & Martinez Santos, Juan Carlos. (2021). Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models. Journal of Physics: Conference Series. 2090. 012171. 10.1088/1742-6596/2090/1/012171. 10.1088/1742-6596/2090/1/012171 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/10620 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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openAccess |
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12 Páginas |
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Pdf |
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application/pdf |
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Journal of Physics: Conference Series - Vol. 2090 (2021). |
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Payares, Estebane0aac5f6-3be2-4211-a602-9a0f32c4602dMartínez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe773182022-03-14T20:59:01Z2022-03-14T20:59:01Z2021-12-102022-03-11Payares, Esteban & Martinez Santos, Juan Carlos. (2021). Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models. Journal of Physics: Conference Series. 2090. 012171. 10.1088/1742-6596/2090/1/012171.https://hdl.handle.net/20.500.12585/1062010.1088/1742-6596/2090/1/012171Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe paradigm of Quantum computing and artificial intelligence has been growing steadily in recent years and given the potential of this technology by recognizing the computer as a physical system that can take advantage of quantum mechanics for solving problems faster, more efficiently, and accurately. We suggest experimentation of this potential through an architecture of different quantum models computed in parallel. In this work, we present encouraging results of how it is possible to use Quantum Processing Units analogically to Graphics Processing Units to accelerate algorithms and improve the performance of machine learning models through three experiments. The first experiment was a reproduction of a parity function, allowing us to see how the convergence of a given Quantum model is influenced significantly by computing it in parallel. For the second and third experiments, we implemented an image classification problem by training quantum neural networks and using pre-trained models to compare their performances with the same experiments carried out with parallel quantum computations. We obtained very similar results in the accuracies, which were close to 100% and significantly improved the execution time, approximately 15 times faster in the best-case scenario. We also propose an alternative as a proof of concept to address emotion recognition problems using optimization algorithms and how execution times can be positively affected by parallel quantum computation. To do this, we use tools such as the cross-platform software library PennyLane and Amazon Web Services to access high-end simulators with Amazon Braket and IBM quantum experience.12 PáginasPdfapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Series - Vol. 2090 (2021).Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Modelsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Parallel QuantumComputation ApproachCartagena de IndiasScott L R, Clark T and Bagheri B 2021 Scientific parallel computing (Princeton University Press)Alerstam E, Svensson T and Andersson-Engels S 2008 Journal of biomedical optics 13 060504Cybenko G 2017 Parallel computing for machine learning in social network analysis 2017 IEEE International 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Trevithick M D, Vainsencher A, Villalonga B, White T, Yao Z J, Yeh P, Zalcman A, Neven H and Martinis J M 2019 Nature 574 505–510 ISSN 1476-4687Rist`e D, da Silva M P, Ryan C A, Cross A W, C´orcoles A D, Smolin J A, Gambetta J M, Chow J M and Johnson B R 2017 npj Quantum Information 3 1–5 ISSN 2056-6387 URL https://www.nature.com/ articles/s41534-017-0017-3Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N and Lloyd S 2017 Nature 549 195–202Schuld M, Sinayskiy I and Petruccione F 2015 Contemporary Physics 56 172–185] Schuld M, Sinayskiy I and Petruccione F 2015 Contemporary Physics 56 172–185 [9] Kerenidis I and Luongo A 2020 Physical Review A 101 062327 ISSN 2469-9926, 2469-9934 arXiv: 1805.08837 URL http://arxiv.org/abs/1805.08837Rebentrost P, Mohseni M and Lloyd S 2014 Physical Review Letters 113 130503 ISSN 0031-9007, 1079-7114 URL https://link.aps.org/doi/10.1103/PhysRevLett.113.130503Havl´ıˇcek V, C´orcoles A D, Temme K, Harrow A W, Kandala A, Chow J M and Gambetta J M 2019 Nature 567 209–212 ISSN 0028-0836, 1476-4687 URL http://www.nature.com/articles/s41586-019-0980-2Li Y, Zhou R G, Xu R, Luo J and Hu W 2020 Quantum Science and Technology 5 044003 URL https://doi.org/10.1088/2058-9565/ab9f93Mari A, Bromley T R, Izaac J, Schuld M and Killoran N 2020 Quantum 4 340 ISSN 2521-327X URL http://dx.doi.org/10.22331/q-2020-10-09-340Mengoni R, Incudini M and Di Pierro A 2021 Quantum Machine Intelligence 3 8 ISSN 2524-4906, 2524-4914 URL http://link.springer.com/10.1007/s42484-020-00035-5Payares E and Martinez-Santos J C 2021 Quantum machine learning for intrusion detection of distributed denial of service attacks: a comparative overview Quantum Computing, Communication, and Simulation ed Hemmer P R and Migdall A L (Online Only, United States: SPIE) p 47 ISBN 9781510642331 9781510642348 URL https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11699/ 2593297/Quantum- machine- learning- for- intrusion- detection- of- distributed- denial- of/10. 1117/12.2593297.fullKilloran N, Bromley T R, Arrazola J M, Schuld M, Quesada N and Lloyd S 2019 Physical Review Research 1 033063 ISSN 2643-1564 URL https://link.aps.org/doi/10.1103/PhysRevResearch.1.033063Bharti K, Cervera-Lierta A, Kyaw T H, Haug T, Alperin-Lea S, Anand A, Degroote M, Heimonen H, Kottmann J S, Menke T, Mok W K, Sim S, Kwek L C and Aspuru-Guzik A 2021 Noisy intermediate-scale quantum (nisq) algorithms (Preprint 2101.08448)Preskill J 2018 Quantum 2 79 ISSN 2521-327X URL http://dx.doi.org/10.22331/q-2018-08-06-79Bergholm V, Izaac J, Schuld M, Gogolin C, Alam M S, Ahmed S, Arrazola J M, Blank C, Delgado A, Jahangiri S, McKiernan K, Meyer J J, Niu Z, Sz´ava A and Killoran N 2020 arXiv:1811.04968 [physics, physics:quant-ph] ArXiv: 1811.04968 URL http://arxiv.org/abs/1811.04968Nielsen M A and Chuang I L 2011 Quantum Computation and Quantum Information: 10th Anniversary Edition hardcover ed (Cambridge University Press) ISBN 978-1107002173] Moore C and Nilsson M 2001 SIAM Journal on Computing 31 799–815 URL https://doi.org/10.1137/ s0097539799355053La Cour B R, Andrew Lanham S and Ostrove C I 2018 2018 IEEE International Conference on Rebooting Computing (ICRC) URL http://dx.doi.org/10.1109/ICRC.2018.8638597Schuld M, Bocharov A, Svore K M and Wiebe N 2020 Physical Review A 101 ISSN 2469-9934 URL http://dx.doi.org/10.1103/PhysRevA.101.032308Genetics helps determine the shape of a person’s face. 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