Optics (physics.optics)

Abstract: We employ unsupervised machine learning to enhance the accuracy of our recently presented scaling method for wave confinement analysis [1]. %The accuracy of the scaling method decreases for systems of small size, which are however the most interesting ones both experimentally and computationally. We employ the standard k-means++ algorithm as well as our own model-based algorithm. We investigate cluster validity indices as a means to find the correct number of confinement dimensionalities to be used as an input to the clustering algorithms. Subsequently, we analyze the performance of the two clustering algorithms when compared to the direct application of the scaling method without clustering. We find that the clustering approach provides more physically meaningful results, but may struggle with identifying the correct set of confinement dimensionalities. We conclude that the most accurate outcome is obtained by first applying the direct scaling to find the correct set of confinement dimensionalities and subsequently employing clustering to refine the results. Moreover, our model-based algorithm outperforms the standard k-means++ clustering.

Abstract: Introduction of controllable deformations into periodic materials that lead to disclinations in their structure opens novel routes for construction of higher-order topological insulators hosting topological states at disclinations. Appearance of these topological states is consistent with the bulk-disclination correspondence principle, and is due to the filling anomaly that results in fractional charges to the boundary unit cells. So far, topological disclination states were observed only in the linear regime, while the interplay between nonlinearity and topology in the systems with disclinations has been never studied experimentally. We report here bon the experimental observation of the nonlinear photonic disclination states in waveguide arrays with pentagonal or heptagonal disclination cores inscribed in transparent optical medium using the fs-laser writing technique. The transition between nontopological and topological phases in such structures is controlled by the Kekul\'e distortion coefficient $r$ with topological phase hosting simultaneously disclination states at the inner disclination core and spatially separated from them corner, zero-energy, and extended edge states at the outer edge of the structure. We show that the robust nonlinear disclination states bifurcate from their linear counterparts and that location of their propagation constants in the gap and, hence, their spatial localization can be controlled by their power. Nonlinear disclination states can be efficiently excited by Gaussian input beams, but only if they are focused into the waveguides belonging to the disclination core, where such topological states reside.

Abstract: The input power-induced transformation of the transverse intensity profile at the output of graded-index multimode optical fibers from speckles into a bell-shaped beam sitting on a low intensity background is known as spatial beam self-cleaning. Its remarkable properties are the output beam brightness improvement and robustness to fiber bending and squeezing. These properties permit to overcome the limitations of multimode fibers in terms of low output beam quality, which is very promising for a host of technological applications. In this review, we outline recent progress in the understanding of spatial beam self-cleaning, which can be seen as a state of thermal equilibrium in the complex process of modal four-wave mixing. In other words, the associated nonlinear redistribution of the mode powers which ultimately favors the fundamental mode of the fiber can be described in the framework of statistical mechanics applied to the gas of photons populating the fiber modes. On the one hand, this description has been corroborated by a series of experiments by different groups. On the other hand, some open issues still remain, and we offer a perspective for future studies in this emerging and controversial field of research.

Abstract: We provide a theoretical framework based on classical electromagnetism, to describe optical properties of Fabry-P\'erot cavities, filled with multilayered and linear chiral materials. We find a formal link between transfer-matrix, scattering-matrix and Green-function approaches to compute the polarization-dependent optical transmission, and cavity-modified circular dichroism signals. We show how general symmetries like Lorentz reciprocity and time-reversal symmetry constrain the modelling of such cavities. We apply this approach to investigate numerically and analytically the properties of various Fabry-P\'erot cavities, made of either metallic or helicity-preserving dielectric photonic crystal mirrors. In the latter case, we analyze the onset of chiral cavity-polaritons in terms of partial helicity-preservation of electromagnetic waves reflected at the mirrors interfaces. Our approach is relevant for designing innovative Fabry-P\'erot cavities for chiral-sensing, and for probing cavity-modified stereochemistry.

Abstract: Spectral computational methods leverage modal or nonlocal representations of data, and a physically realized approach to spectral computation pertains to encoded diffraction. Encoded diffraction offers a hybrid approach that pairs analog wave propagation with digital back-end electronics, however the intermediate sensor patterns are correlations rather than linear signal weights, which limits the development of robust and efficient downstream analyses. Here, with vortex encoders, we show that the solution for the signal field from sensor intensity adopts the form of polynomial regression, which is subsequently solved with a learned, linear transformation. This result establishes an analytic rationale for a spectral-methods paradigm in physically realized machine learning systems. To demonstrate this paradigm, we quantify the learning that is transferred with an image basis using speckle parameters, Singular-Value Decomposition Entropy ($H_{SVD}$) and Speckle-Analogue Density (SAD). We show that $H_{SVD}$, a proxy for image complexity, indicates the rate at which a model converges. Similarly, SAD, an averaged spatial frequency, marks a threshold for structurally similar reconstruction. With a vortex encoder, this approach with parameterized training may be extended to distill features. In fact, with images reconstructed with our models, we achieve classification accuracies that rival decade-old, state-of-the-art computer algorithms. This means that the process of learning compressed spectral correlations distills features to aid image classification, even when the goal images are feature-agnostic speckles. Our work highlights opportunities for analytic and axiom-driven machine-learning designs appropriate for real-time applications.

Abstract: Coherent beam combination is one promising way to overcome the power limit of one single laser. In this paper, we use a Multi-Plane Light Converter to combine 12 fibers at 1.03 micron with a phase locking setup. The overall loss measurement gives a combination efficiency in the fundamental Hermite-Gaussian mode as high as 70%. This setup can generate the fundamental and higher-order Hermite-Gaussian modes and has beam steering capabilities.

Abstract: Spatial modes of light have become highly attractive to increase the dimension and, thereby, security and information capacity in quantum key distribution (QKD). So far, only transverse electric field components have been considered, while longitudinal polarization components have remained neglected. Here, we present an approach to include all three spatial dimensions of electric field oscillation in QKD by implementing our tunable, on-a-chip vector beam decoder (VBD). This inversely designed device pioneers the "preparation" and "measurement" of three-dimensionally polarized mutually unbiased basis states for high-dimensional (HD) QKD and paves the way for the integration of HD QKD with spatial modes in multifunctional on-a-chip photonics platforms.