Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2409.00998 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917981633642496 |
|---|---|
| author | De Lorenzis, A. Casado, M. P. Estarellas, M. P. Gullo, N. Lo Lux, T. Plastina, F. Riera, A. Settino, J. |
| author_facet | De Lorenzis, A. Casado, M. P. Estarellas, M. P. Gullo, N. Lo Lux, T. Plastina, F. Riera, A. Settino, J. |
| contents | Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine learning techniques for image classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyse different phases of the quantum extreme learning machine process, from the dataset preparation to the image final classification. In particular, we have tested different encodings, together with Principal Component Analysis, the use of Auto-Encoders, as well as the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. Additionally, while different encodings can lead to significantly different performances, Hamiltonians with varying degrees of connectivity exhibit the same discrimination rate, provided they are interacting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00998 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Harnessing Quantum Extreme Learning Machines for image classification De Lorenzis, A. Casado, M. P. Estarellas, M. P. Gullo, N. Lo Lux, T. Plastina, F. Riera, A. Settino, J. Quantum Physics Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine learning techniques for image classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyse different phases of the quantum extreme learning machine process, from the dataset preparation to the image final classification. In particular, we have tested different encodings, together with Principal Component Analysis, the use of Auto-Encoders, as well as the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. Additionally, while different encodings can lead to significantly different performances, Hamiltonians with varying degrees of connectivity exhibit the same discrimination rate, provided they are interacting. |
| title | Harnessing Quantum Extreme Learning Machines for image classification |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.00998 |