Neuromorphic physics combines brain-inspired methods with physics for efficient information processing. By using nanomaterials and technologies such as spintronics, superconductivity, and photonics, it enhances the performance of classical and quantum neural networks. Physics plays a role in both refining neuron- and synapse-mimicking devices and in improving algorithms. The goal is to design energy-saving devices capable of analyzing complex data and learning in real-time.

Our main research topics are Nano-neuromorphic computing (with spintronic and electronic nano-devices), Quantum neuromorphic computing (with superconducting circuits) and Neuromorphic algorithms.