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.

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 propagation of light in nonlinear optical media has been widely used as a tabletop platform for emulating quantum-like phenomena due to their similar theoretical description to quantum fluids. These fluids of light are often used to study two-dimensional phenomena involving superfluid-like flows, yet turbulent regimes still remain underexplored. In this work, we study the possibility of creating two-dimensional turbulent phenomena and probing their signatures in the kinetic energy spectrum. To that end, we emulate and disturb a fluid of light with an all-optical defect using the propagation of two beams in a photorefractive crystal. Our experimental results show that the superfluid regime of the fluid of light breaks down at a critical velocity at which the defect starts to exert a drag force on the fluid, in accordance with the theoretical and numerical predictions. Furthermore, in this dissipative regime, nonlinear perturbations are excited on the fluid that can decay into vortex structures and thus precede a turbulent state. Using the off-axis digital holography method, we reconstructed the complex description of the output fluids and calculated the incompressible component of the kinetic energy. With these states, we observed the expected power law that characterizes the generated turbulent vortex dipole structures. The findings enclosed in this manuscript align with the theoretical predictions for the vortex structures of two-dimensional quantum fluids and thus may pave the way to the observation of other distinct hallmarks of turbulent phenomena, such as distinct turbulent regimes and their associated power laws and energy cascades.

Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

This paper reports the development of a numerical module for the HiLight simulation platform dedicated to the propagation of light in nonlocal nonlinear optical media and the adaptions implemented for it to be used as a numerical test-bed to evaluate the impact of new extensions of the Theory of General Relativity in the dynamics of a N-body system. The phenomenology of light in nonlocal and nonlinear media is very rich and can be described by a multitude of effective models, with different levels of detail and approximations, which coincide with few or no differences with those found in many other fields of physics. In particular, nonlocal extensions of the Generalized Nonlinear Schr¨odinger equation (also known as the generalized Schr¨odinger-Newton system) constitute a wide class of physical models that can be found in both optics and in the studies of alternative theories for gravity. Therefore, numerical solvers developed for the former can be adapted to address the later. Indeed, this paper discusses the adaptation of a numerical solver of the generalized Schr¨odinger-Newton system based on GPGPU supercomputing, initially developed to investigate the properties of light in exotic nonlocal media, to tackle the dynamics of large distributions of matter whose interaction is governed by extensions of the Theory of General Relativity, namely those based on non-minimal coupling between curvature and matter. This paper analysis the structure of the resulting simulation module, its performance and validation tests.

Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.