The time crystal is an eagerly sought phase of matter, a many-body strongly correlated system with broken time-translation symmetry and ergodicity. We demonstrate that a classical metamaterial nanostructure - a two-dimensional array of plasmonic metamolecules supported on nanowires - exhibit complex picometer scale dynamics in presence of light. It can be driven to a state possessing all the key features of a continuous space-time crystal: continuous coherent illumination by light resonant with the metamolecules’ plasmonic mode triggers a spontaneous first order phase transition to a superradiant-like state of transmissivity oscillations, resulting from many-body interactions among the metamolecules. The space-time crystal is characterized by long-range order in space and time, broken ergodicity and reduced spectral entropy that are driven by non-reciprocal non-Hamiltonian forces of light pressure.
In this paper, the advancements in structured light beams recognition using speckle-based convolutional neural networks (CNNs) have been presented. Speckle fields, generated by the interference of multiple wavefronts diffracted and scattered through a diffuser, project a random distribution. The generated random distribution of phase and intensity correlates to the structured light beam of the corresponding speckle field. This unique distribution of phase and intensity offers an additional dimension for recognizing the encoded information in structured light. The CNNs are well-suited for harnessing this unique ability to recognize the speckle field by learning hidden patterns within data. One notable advantage of speckle-based recognition is their ability to identify structured light beams from a small portion of the speckle field, even in high noise environments. The diffractive nature of the speckle field enables off-axis recognition, showcasing its capability in information broadcasting employing structured light beams. This is a significant departure from direct-mode detection-based models to alignment-free speckle-based detection models, which are no longer constrained by the directionality of laser beams.
We have demonstrated that a classical metamaterial nanostructure, a two-dimensional array of plasmonic metamolecules supported on flexible nanowires, can be driven to a state possessing all the key features of a continuous time crystal - an eagerly-sought state of matter with properties periodic in time, rather than in space. The phenomenon points to the possibility of a new mechanism for the synchronization of noise-driven, linear oscillators based upon non-reciprocal coupling (non-Hamiltonian optical forces) between metamolecules.
Intensity degenerate orbital angular momentum (OAM) modes are impossible to recognize by direct visual inspection even using available machine learning techniques. We are reporting speckle-learned convolutional neural network (CNN) for the recognition of intensity degenerate Laguerre–Gaussian (LGp , l) modes, intensity degenerate LG superposition modes, and intensity degenerate perfect optical vortices. The CNN is trained on the simulated one-dimensional far-field intensity speckle patterns of the corresponding intensity degenerate OAM modes. The trained CNN recognizes intensity degenerate OAM modes with an accuracy >99 % . Speckle-learned CNNs are also capable of recognizing intensity degenerate OAM modes even under the presence of high Gaussian white noise and atmospheric turbulence with an accuracy >97 % .
Machine learning has emerged as a powerful tool for physicists for building empirical models from the data. We exploit two convolutional networks, namely Alexnet and wavelet scattering network for the classification of orbital angular momentum (OAM) beams. We present a comparative study of these two methods for the classification of 16 OAM modes having radial and azimuthal phase profiles and eight OAM superposition modes with and without atmospheric turbulence effects. Instead of direct OAM intensity images, we have used the corresponding speckle intensities as an input to the model. Our study demonstrates a noise and alignment-free OAM mode classifier having maximum accuracy of >94 % and >99 % for with and without turbulence, respectively. The main advantage of this method is that the mode classification can be done by capturing a small region of the speckle intensity having a sufficient number of speckle grains. We also discuss this smallest region that needs to be captured and the optimal resolution of the detector required for mode classification.
Orbital angular momentum (OAM) beams have the potential to increase the information-carrying capacity because of the extra degrees of freedom associated with them. Traditional methods for mode detection and de-multiplexing are complex and require expensive optical hardware. We propose a very simple and cost effective deep learning based model for demultiplexing OAM modes at the receiver. In this method we have used a random phase mask of known inhomogeneity to generate a scattered field of OAM mode and the intensity images of these scattered field are used as an input to the Convolutional Neural Network. The model is trained for various Laguerre-Gaussian (𝐿𝐺𝑝𝑙) modes carrying OAM with 𝑝 = 0 and 𝑙 = 1,2,3,4,5,6,7,8. The model is tested for various set of images and the overall accuracy of each dataset is <99%. To demonstrate the proof of concept we simulated an experiment to generate the speckle field at the receiver of optical communication system for demultiplexing OAM modes and decoding the 3-bit information.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.