Quantum deep transfer learning
Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2021-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ac2a5e |
| _version_ | 1827872633051414528 |
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| author | Longhan Wang Yifan Sun Xiangdong Zhang |
| author_facet | Longhan Wang Yifan Sun Xiangdong Zhang |
| author_sort | Longhan Wang |
| collection | DOAJ |
| description | Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms. |
| first_indexed | 2024-03-12T16:26:33Z |
| format | Article |
| id | doaj.art-888ad295d10b4f93af22236f14317347 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T16:26:33Z |
| publishDate | 2021-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-888ad295d10b4f93af22236f143173472023-08-08T15:41:58ZengIOP PublishingNew Journal of Physics1367-26302021-01-01231010301010.1088/1367-2630/ac2a5eQuantum deep transfer learningLonghan Wang0Yifan Sun1https://orcid.org/0000-0003-4935-630XXiangdong Zhang2https://orcid.org/0000-0002-7725-8814Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology , 100081, Beijing, People’s Republic of ChinaKey Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology , 100081, Beijing, People’s Republic of ChinaKey Laboratory of Advanced Optoelectronic Quantum Architecture and Measurements of Ministry of Education, Beijing Key Laboratory of Nanophotonics & Ultrafine Optoelectronic Systems, School of Physics, Beijing Institute of Technology , 100081, Beijing, People’s Republic of ChinaQuantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms.https://doi.org/10.1088/1367-2630/ac2a5equantum transfer learningquantum machine learningquantum computation |
| spellingShingle | Longhan Wang Yifan Sun Xiangdong Zhang Quantum deep transfer learning New Journal of Physics quantum transfer learning quantum machine learning quantum computation |
| title | Quantum deep transfer learning |
| title_full | Quantum deep transfer learning |
| title_fullStr | Quantum deep transfer learning |
| title_full_unstemmed | Quantum deep transfer learning |
| title_short | Quantum deep transfer learning |
| title_sort | quantum deep transfer learning |
| topic | quantum transfer learning quantum machine learning quantum computation |
| url | https://doi.org/10.1088/1367-2630/ac2a5e |
| work_keys_str_mv | AT longhanwang quantumdeeptransferlearning AT yifansun quantumdeeptransferlearning AT xiangdongzhang quantumdeeptransferlearning |