Related papers: Dissipative learning of a quantum classifier

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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