  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.
