Dissipative learning of a quantum classifier
arXiv:2307.12293 · doi:10.1007/s12043-023-02653-7
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.
8 pages, 5 figures
References in corpus (4)
- Deep Learning in Neural Networks: An Overview
- A simple example of "Quantum Darwinism": Redundant information storage in many-spin environments
- Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
- Redundancy of einselected information in quantum Darwinism: The irrelevance of irrelevant environment bits