Photons carrying a well-defined orbital angular momentum have been proven to modify spectroscopic selection rules in atomic matter. Excitation profiles of electric quadrupole transitions have been measured with single trapped 40 Ca + ions for varying polarizations. We further develop the photo-absorption formalism to study the case of arbitrary alignment of the beam's optical axis with respect to the ion's quantization axis and mixed multipolarity. Thus, predictions for M1-dominated 40 Ar 13+ , E3-driven 171 Yb + and 172 Yb + , and B-like 20 Ne 5+ are presented. The latter case displays novel effects, coming from the presence of a strong photonmagnetic dipole coupling.

While information is ubiquitously generated, shared, and analyzed in a modern-day life, there is still some controversy around the ways to assess the amount and quality of information inside a noisy optical channel. A number of theoretical approaches based on, e.g., conditional Shannon entropy and Fisher information have been developed, along with some experimental validations. Some of these approaches are limited to a certain alphabet, while others tend to fall short when considering optical beams with a nontrivial structure, such as Hermite-Gauss, Laguerre-Gauss, and other modes with a nontrivial structure. Here, we propose a new definition of the classical Shannon information via the Wigner distribution function, while respecting the Heisenberg inequality. Following this definition, we calculate the amount of information in Gaussian, Hermite-Gaussian, and Laguerre-Gaussian laser modes in juxtaposition and experimentally validate it by reconstruction of the Wigner distribution function from the intensity distribution of structured laser beams. We experimentally demonstrate the technique that allows to infer field structure of the laser beams in singular optics to assess the amount of contained information. Given the generality, this approach of defining information via analyzing the beam complexity is applicable to laser modes of any topology that can be described by well-behaved functions. Classical Shannon information, defined in this way, is detached from a particular alphabet, i.e., communication scheme, and scales with the structural complexity of the system. Such a synergy between the Wigner distribution function encompassing the information in both real and reciprocal space and information being a measure of disorder can contribute into future coherent detection algorithms and remote sensing.

Decision-making through artificial neural networks with minimal latency is critical for numerous applications such as navigation, tracking, and real-time machine action systems. This requires machine learning hardware to process multidimensional data at high throughput. Unfortunately, handling convolution operations, the primary computational tool for data classification tasks, obeys challenging runtime complexity scaling laws. However, homomorphically implementing the convolution theorem in a Fourier optics display light processor can achieve a non-iterative (1) runtime complexity for data inputs beyond 1,000 × 1,000 large matrices. Following this approach, here we demonstrate data streaming multi-kernel image batching using a Fourier Convolutional Neural Network (FCNN) accelerator. We show image batch processing of large-scale matrices as 2 million dot product multiplications performed by a digital light processing module in the Fourier domain. Furthermore, we further parallelize this optical FCNN system by exploiting multiple spatially parallel diffraction orders, achieving a 98x throughput improvement over state-of-the-art FCNN accelerators. A comprehensive discussion of the practical challenges associated with working at the edge of system capabilities highlights the problem of crosstalk and resolution scaling laws in the Fourier domain. Accelerating convolution by exploiting massive parallelism in display technology brings non-Van Neumann-based machine learning acceleration.

Convolutional neural networks are paramount in image and signal processing, and are responsible for the majority of image recognition power consumption today, concentrated mainly in convolution computations. With convolution operations being computationally intensive, next‐generation hardware accelerators need to offer parallelization and high efficiency. Diffractive optics offers the promise of low‐latency, highly parallel convolution operations. However, thus far parallelism is only partially harvested, thereby significantly underdelivering in comparison to its throughput potential. Here, a parallelized operation high‐throughput Fourier optic convolutional accelerator is demonstrated. For the first time, simultaneous processing of multiple kernels in Fourier domain enabled by optical diffraction orders is achieved alongside input parallelism. The proposed approach can offer ≈100× speedup over the previous generation optical diffraction‐based processor and 10× speedup over other state‐of‐the‐art optical Fourier classifiers.

We introduce a wave-like exponential neural network (ENN) which is just enough to accurately describe optical systems. Results are compared to a fully connected neural network.