Information-driven Nonlinear Quantum Neuron

TL;DR

This paper proposes a hardware-efficient quantum neural network model that operates as an open quantum system, capturing non-linear behavior essential for neural network functionality, and demonstrates its compatibility with learning processes.

Contribution

It introduces a novel dissipative quantum neural network model based on repeated interactions that exhibits differentiable, non-linear activation functions, addressing a key challenge in quantum neural network design.

Findings

Model exhibits non-linear, differentiable activation functions.

Compatible with quantum learning processes.

Operates efficiently as an open quantum system.

Abstract

The promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circuit model, and implementing them based on hardware. However, the ability to capture the non-linear behavior in neural networks using a computation process that usually involves linear quantum mechanics principles remains a major challenge in both categories. In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed, which presents non-linear behaviour. The model's compatibility with learning processes is tested through the obtained analytical results. In other words, we show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.