Dissipative learning of a quantum classifier

TL;DR

This paper investigates a quantum classifier modeled as an open quantum system, demonstrating successful training via gradient descent and highlighting potential for differentiable activation functions in quantum machine learning.

Contribution

It introduces a quantum classifier based on dissipative dynamics and shows its trainability with gradient descent, offering a new approach beyond standard quantum circuit models.

Findings

Successfully trained the quantum classifier with gradient descent

Continuous dynamics suggest potential for differentiable activation functions

Open quantum system approach offers an alternative to standard models

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.