Information-driven nonlinear quantum neuron

Quantum computing has motivated the development of quantum neural-network models, yet generating nonlinear activation behavior within quantum dynamics remains a central challenge. In this study, we propose a quantum neuron model operating as an open quantum system and exhibiting nonlinear activation through dissipative repeated interactions. The activation is generated by the steady-state response of a probe quantum system interacting with information reservoirs, rather than by fitting an externally prescribed nonlinear function. We show that the activation steepness can be tuned by the probe spin number J, providing a physical control parameter for the sigmoid-like response. The model's suitability for learning is verified through analytical results. The resulting framework allows easy parametrization of input quantum information and produces differentiable, nonlinear activation functions.