A mathematical framework for quantum neural networks

Abstract

[en] This thesis focuses on dissipative quantum neural networks, a subfield of quantum machine learning. Classical neural networks and the fundamental concepts of quantum mechanics, crucial for understanding quantum machine learning, are introduced from a mathematical perspective. We exhibit the parallelism between the training algorithms of classical neural networks and dissipative quantum neural networks and establish a mathematical framework to describe classical and quantum neural networks.