Photonic extreme learning machine by free-space optical propagation

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme – photonic extreme learning machines – that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.


Code for multilevel quantum gates now available on Github

We made available our Python and TensorFlow code about machine learning design of multilevel quantum gates with reservoir computing

GitHub Repository

See also

MARGO project startup

Our new project within the Graphene Flagship ( Flagera Call JTC 2019) officially started !

MARGO stands for MAxillofacial bone Regeneration by 3D-printed laser-activated Graphene Oxide Scaffolds

MARGO is an exciting data-driven interdisciplinary research on our previous results on the Antibacterial coating and stem cell replication by Graphene Oxide, following our ERC PoC Project VANGUARD!

See also

MARGO project website