Reservoir computing is a promising framework that facilitates the approach to physical neuromorphic hardware by enabling a given nonlinear physical system to act as a computing platform. In this work, we exploit this paradigm to propose a versatile and robust soliton-based computing system using a discrete soliton chain as a reservoir. By taking advantage of its tunable governing dynamics, we show that sufficiently strong nonlinear dynamics allows our soliton-based solution to perform accurate regression and classification tasks of non-linear separable datasets. At a conceptual level, the results presented pave a way for the physical realization of novel hardware solutions and have the potential to inspire future research on soliton-based computing using various physical platforms, leveraging its ubiquity across multiple fields of science, from nonlinear optical media to quantum systems.

Optical analog experiments have captured a lot of interest in recent years by offering a strategy to test theoretical models and concepts that would be otherwise untestable. The approach relies on the similarity between the mathematical model for light propagation in nonlinear optical media and the model to be mimicked. In particular, the analogy between light and a quantum fluid with superfluidlike properties has been studied extensively. Still, while most of these studies use thermo-optical media to perform these experiments, the possibility of using nematic liquid crystals to perform such optical analog experiments remains to be analyzed. This work explores how this medium can constitute an alternative to materials more commonly used in optical analogs, such as thermo-optical media, and how its tunable properties can be advantageous to explore and better control fluidlike properties of light. Moreover, we explore the analogy between the propagation of light and a quantum fluid, and propose a pump-probe experiment to measure the dispersion relation of the superfluid analog.

In this work, we investigate the superfluidic properties of light propagating in a four-level coherent atomic medium. The model is derived under the paraxial approximation in the form of a generalized nonlinear Schrödinger equation and features spatially controllable and quantum-enhanced optical properties, which can offer new possibilities in the field of optical analogue systems. In particular, we use this versatility to study the dynamics of an optical vortex beam confined in a nontrivial connected geometry, finding numerical evidence of another superfluidic signature analogue: the persistent current of light.

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

The generalized Schrödinger-Newton system of equations with both local and nonlocal nonlinearities is widely used to describe light propagating in nonlinear media under the paraxial approximation. However, its use is not limited to optical systems and can be found to describe a plethora of different physical phenomena, for example, dark matter or alternative theories for gravity. Thus, the numerical solvers developed for studying light propagating under this model can be adapted to address these other phenomena. Indeed, in this work we report the development of a solver for the HiLight simulations platform based on GPGPU supercomputing and the required adaptations for this solver to be used to test the impact of new extensions of the Theory of General Relativity in the dynamics of the systems. In this work we shall analyze theories with nonminimal coupling between curvature and matter. This approach in the study of these new models offers a quick way to validate them since their analytical analysis is difficult. The simulation module, its performance, and some preliminary tests are presented in this paper.

The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.