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.
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.
We propose the use of artificial neural networks to design and characterize photonic topological insulators. As a hallmark, the band structures of these systems show the key feature of the emergence of edge states, with energies lying within the energy gap of the bulk materials and localized at the boundary between regions of distinct topological invariants. We consider different structures such as one-dimensional photonic crystals, PT-symmetric chains and cylindrical systems and show how, through a machine learning application, one can identify the parameters of a complex topological insulator to obtain protected edge states at target frequencies. We show how artificial neural networks can be used to solve the long standing quest of inverse-problems solution and apply it to the cutting edge topic of topological nanophotonics.
Pilozzi et al 2020 Nanotechnology https://doi.org/10.1088/1361-6528/abd508