Abstract
The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyse the learning dynamics of a quantum classifier model that works as an open quantum system which is an alternative to the standard quantum circuit model. According to the obtained results, the model can be successfully trained with a gradient descent (GD)-based algorithm. The fact that these optimisation processes have been obtained with continuous dynamics, shows promise for the development of a differentiable activation function for the classifier model.
Original language | English |
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Article number | 165 |
Journal | Pramana - Journal of Physics |
Volume | 97 |
Issue number | 4 |
DOIs | |
Publication status | Published - Dec 2023 |
Bibliographical note
Publisher Copyright:© 2023, Indian Academy of Sciences.
Keywords
- 03.65.Yz
- 03.67.Ac
- 03.67.Lx
- 03.67.−a
- Quantum learning
- cost function
- open quantum system
- quantum classifier
- training