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Integrated diffraction gratings offer a compact route to magneto-optical traps (MOTs) for atom cooling and trapping, thus preparing MOTs for future scalable quantum systems. While segmented tri-gratings ensure axial radiation pressure balance, they are limited in optical trapping volume. Planar 2D gratings, though offer larger trapping regions, suffer from low diffraction efficiency and the resulting axial pressure imbalance, necessitating the use of a neutral density (ND) filter to achieve this balance. We present a numerically optimized 2D diffraction grating design that overcomes these limitations and satisfies the required optical conditions for laser cooling, namely, radiation pressure balance, specular reflection cancellation, and circular polarization handedness reversal upon diffraction, thus achieving an optical molasses – a necessary condition in MOT. Using Rigorous Coupled Wave Analysis (RCWA) and a Genetic Algorithm (GA), we design a grating for (_ ^87)Rb grating MOT (GMOT) that achieves a 24% first-order diffraction efficiency, of which 99.7% have the correct circular handedness. These properties enable efficient atom cooling without an ND filter when used with a flat-top beam inside the vacuum chamber. Our design simplifies optical alignment, reduces system footprint, and advances the integration of GMOTs into compact quantum devices. 

Abstract Diffractive Neural Networks (DNNs) leverage the power of light to enhance computational performance in machine learning, offering a pathway to high-speed, low-energy, and large-scale neural information processing. However, most existing DNN architectures are optimized for single tasks and thus lack the flexibility required for the simultaneous execution of multiple tasks within a unified artificial intelligence platform. In this work, we utilize the polarization and wavelength degrees of freedom of light to achieve optical multi-task identification using the MNIST, FMNIST, and KMNIST datasets. Employing bilayer cascaded metasurfaces, we construct dual-channel DNNs capable of simultaneously classifying two tasks, using polarization and wavelength multiplexing schemes through a meta-atom library. Numerical evaluations demonstrate performance accuracies comparable to those of individually trained single-channel, single-task DNNs. Extending this approach to three-task parallel recognition reveals an expected performance decline yet maintains satisfactory classification accuracies of greater than 80 % for all tasks. We further introduce a novel end-to-end joint optimization framework to redesign the three-task classifier, demonstrating substantial improvements over the meta-atom library design and offering the potential for future multi-channel DNN designs. Our study could pave the way for the development of ultrathin, high-speed, and high-throughput optical neural computing systems. 

Abstract Hyperbolic metamaterial (HMM) is a unique type of anisotropic material that can exhibit metal and dielectric properties at the same time. This unique characteristic results in it having unbounded isofrequency surface contours, leading to exotic phenomena such as spontaneous emission enhancement and applications such as super-resolution imaging. However, at optical frequencies, HMM must be artificially engineered and always requires a metal constituent, whose intrinsic loss significantly limits the experimentally accessible wave vector values, thus negatively impacting the performance of these applications. The need to reduce loss in HMM stimulated the development of the second-generation HMM, termed active HMM, where gain materials are utilized to compensate for metal’s intrinsic loss. With the advent of topological photonics that allows robust light transportation immune to disorders and defects, research on HMM also entered the topological regime. Tremendous efforts have been dedicated to exploring the topological transition from elliptical to hyperbolic dispersion and topologically protected edge states in HMM, which also prompted the invention of lossless HMM formed by all-dielectric material. Furthermore, emerging twistronics can also provide a route to manipulate topological transitions in HMMs. In this review, we survey recent progress in topological effects in HMMs and provide prospects on possible future research directions. 

A miniature on-chip laser is an essential component of photonic integrated circuits for a plethora of applications, including optical communication and quantum information processing. However, the contradicting requirements of small footprint, robustness, single-mode operation, and high output power have led to a multi-decade search for the optimal on-chip laser design. During this search, topological phases of matter—conceived initially in electronic materials in condensed matter physics—were successfully extended to photonics and applied to miniature laser designs. Benefiting from the topological protection, a topological edge mode laser can emit more efficiently and more robustly than one emitting from a trivial bulk mode. In addition, single-mode operation over a large range of excitation energies can be achieved by strategically manipulating topological modes in a laser cavity. In this Perspective, we discuss the recent progress of topological on-chip lasers and an outlook on future research directions.