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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2107.09781 |
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| _version_ | 1866909140332314624 |
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| author | Useche, Diego H. Giraldo-Carvajal, Andres Zuluaga-Bucheli, Hernan M. Jaramillo-Villegas, Jose A. González, Fabio A. |
| author_facet | Useche, Diego H. Giraldo-Carvajal, Andres Zuluaga-Bucheli, Hernan M. Jaramillo-Villegas, Jose A. González, Fabio A. |
| contents | This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_09781 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Quantum Measurement Classification with Qudits Useche, Diego H. Giraldo-Carvajal, Andres Zuluaga-Bucheli, Hernan M. Jaramillo-Villegas, Jose A. González, Fabio A. Quantum Physics Machine Learning This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer. |
| title | Quantum Measurement Classification with Qudits |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2107.09781 |