Ising machines are novel computing devices for the energy minimization of Ising models. These combinatorial optimization problems are of paramount importance for science and technology, but remain difficult to tackle on large scale by conventional electronics. Recently, various photonics-based Ising machines demonstrated ultra-fast computing of Ising ground state by data processing through multiple temporal or spatial optical channels. Experimental noise acts as a detrimental effect in many of these devices. On the contrary, we here demonstrate that an optimal noise level enhances the performance of spatial-photonic Ising machines on frustrated spin problems. By controlling the error rate at the detection, we introduce a noisy-feedback mechanism in an Ising machine based on spatial light modulation. We investigate the device performance on systems with hundreds of individually-addressable spins with all-to-all couplings and we found an increased success probability at a specific noise level. The optimal noise amplitude depends on graph properties and size, thus indicating an additional tunable parameter helpful in exploring complex energy landscapes and in avoiding trapping into local minima. The result points out noise as a resource for optical computing. This concept, which also holds in different nanophotonic neural networks, may be crucial in developing novel hardware with optics-enabled parallel architecture for large-scale optimizations.
With an exact recursive approach, we study photonic crystal fibers and resonators with topological features induced by Aubry–Andre–Harper cladding modulation. We find nontrivial gaps and edge states at the interface between regions with different topological invariants. These structures show topological protection against symmetry-preserving local perturbations that do not close the gap and sustain strong field localization and energy concentration at a given radial distance. As topological light guiding and trapping devices, they may bring about many opportunities for both fundamentals and applications unachievable with conventional devices.
Spin and angular momenta of light are important degrees of freedom in nanophotonics which control light propagation, optical forces and information encoding. Typically, optical angular momentum is generated using q-plates or spatial light modulators. Here, we show that graphene-supported plasmonic nanostructures with broken rotational symmetry provide a surprising spin to orbital angular momentum conversion, which can be continuously controlled by changing the electrochemical potential of graphene. Upon resonant illumination by a circularly polarized plane wave, a polygonal array of indium-tin-oxide nanoparticles on a graphene sheet generates scattered field carrying electrically-tunable orbital angular momentum. This unique photonic spin-orbit coupling occurs due to the strong coupling of graphene plasmon polaritons and localised surface plasmons of the nanoparticles and leads to the controlled directional excitation of graphene plasmons. The tuneable spin-orbit conversion pave the way to high-rate information encoding in optical communications, electric steering functionalities in optical tweezers, and nanorouting of higher-dimensional entangled photon states.
The school brings together experts in emerging photonic technologies, machine learning techniques, and fundamental physics who will share with young researchers their knowledge and interdisciplinary approaches for understanding and designing complex photonic systems and their practical applications. In the new era of artificial intelligence, algorithms and computational interfaces are broadly emerging as novel tools to do scientific research. The paradigms of machine learning also inspire interpretations and methodologies, in both theories and experiments. Nonlinear, quantum and bio-photonics, as well as optical communications, are surprisingly influenced by these new ideas. The summer school is aimed to explore machine learning applications in the specific fields of nonlinear optics and photonics.
The areas covered include, but are not limited to: machine learning methods and complexity of optical communication systems, including topics such as the nonlinear Fourier transform and transmission over multimode fibres; complexity in quantum systems emulated in photonics (including optical computing); complexity of emerging novel materials, device and components such as micro-resonators and plasmonic nanostructures. Importantly, the complexity in bio-medical photonic applications will be also considered as a high priority topic.
The summer school will focus on comprehensive review talks from major figures in complementary areas of photonics and machine learning. Invited speakers will deliver one or more 1-hour lectures over 5 days. We shall select up to 30 participants from the pool of most vibrant PhD students and postdocs, with a special focus on Marie Curie Fellows as future leaders of European photonics science and industry.
Machine-learning by rogue waves, dispersive shocks, and solitons
We study artificial neural networks with nonlinear waves as a computing reservoir. We discuss universality and the conditions to learn a dataset in terms of output channels and nonlinearity. A feed-forward three-layer model, with an encoding input layer, a wave layer, and a decoding readout, behaves as a conventional neural network in approximating mathematical functions, real-world datasets, and universal Boolean gates. The rank of the transmission matrix has a fundamental role in assessing the learning abilities of the wave. For a given set of training points, a threshold nonlinearity for universal interpolation exists. When considering the nonlinear Schroedinger equation, the use of highly nonlinear regimes implies that solitons, rogue, and shock waves do have a leading role in training and computing. Our results may enable the realization of novel machine learning devices by using diverse physical systems, as nonlinear optics, hydrodynamics, polaritonics, and Bose-Einstein condensates. The application of these concepts to photonics opens the way to a large class of accelerators and new computational paradigms. In complex wave systems, as multimodal fibers, integrated optical circuits, random, topological devices, and metasurfaces, nonlinear waves can be employed to perform computation and solve complex combinatorial optimization.