Dissipative learning of a quantum classifier
- URL: http://arxiv.org/abs/2307.12293v1
- Date: Sun, 23 Jul 2023 11:08:51 GMT
- Title: Dissipative learning of a quantum classifier
- Authors: Ufuk Korkmaz, Deniz T\"urkpen\c{c}e
- Abstract summary: We analyze the learning dynamics of a quantum classifier model that works as an open quantum system.
The model can be successfully trained with a gradient descent (GD) based algorithm.
The fact that these optimization processes have been obtained with continuous dynamics, shows promise for the development of a differentiable activation function.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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 analyze 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 optimization processes have been obtained
with continuous dynamics, shows promise for the development of a differentiable
activation function for the classifier model.
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