Category: Projects
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
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MARGO project startup
(Image above from Unsplash collection by Spentys )
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!
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Phase space machine learning for multi-particle event optimization in Gaussian boson sampling
We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. We also demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.
https://arxiv.org/abs/2102.12142
Official code