Related papers: Optimization of photonic crystal nanocavities base…

Depth factorization and quantization have emerged as two of the principal strategies for designing efficient deep convolutional neural network (CNN) architectures tailored for low-power inference on the edge. However, there is still little…

We present a systematic workflow for the design of one-dimensional photonic crystal nanobeam cavities with non-zero cavity lengths. By simultaneously optimizing the lattice periodicity, air-hole geometry, and cavity length, our approach…

Inverse design coupled with adjoint optimization is a powerful method to design on-chip nanophotonic devices with multi-wavelength and multi-mode optical functionalities. Although only two simulations are required in each iteration of this…

Stochastic computing allows a drastic reduction in hardware complexity using serial processing of bit streams. While the induced high computing latency can be overcome using integrated optics technology, the design of realistic optical…

In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise convolution to…

We perform optimization of Q-factor in the system of freestanding three/four/five/six coaxial subwavelength dielectric disks over all scales. Each parameter contributes almost one order of magnitude of the Q-factor due to multiple avoided…

Miniaturized autonomous unmanned aerial vehicles (UAVs) are gaining popularity due to their small size, enabling new tasks such as indoor navigation or people monitoring. Nonetheless, their size and simple electronics pose severe challenges…

Although the neural network (NN) technique plays an important role in machine learning, understanding the mechanism of NN models and the transparency of deep learning still require more basic research. In this study, we propose a novel…

The realization of photonic crystals (PC) in diamond is of major importance for the entire field of spintronics based on fluorescent centers in diamond. The processing steps for the case of diamond differ from those commonly used, due to…

The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters. However, existing D-KFAC…

Exploring the dynamics of an optically levitated dielectric micro- and nanoparticle is an exciting new subject in quantum science. Recent years have witnessed rapid advancements in attaining quantum-limited optical detection and control of…

Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering…

The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging…

Active wavelength-scale optoelectronic components are widely used in photonic integrated circuitry, however coherent sources of light -- namely optical lasers -- remain the most challenging component to integrate. Semiconductor nanowire…

Single emitters in solid state are great sources of single and entangled photons. To boost their extraction efficiency and tailor their emission properties, they are often incorporated in photonic nanostructures. However, achieving accurate…

Deep learning for distribution grid optimization can be advocated as a promising solution for near-optimal yet timely inverter dispatch. The principle is to train a deep neural network (DNN) to predict the solutions of an optimal power flow…

Photonic crystal cavities can localize light into nanoscale volumes with high quality factors. This permits a strong interaction between light and matter, which is important for the construction of classical light sources with improved…

Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature maps, which embed classical data into the state space of qubits. We introduce Quantum…

Advanced microscopy and/or spectroscopy tools play indispensable role in nanoscience and nanotechnology research, as it provides rich information about the growth mechanism, chemical compositions, crystallography, and other important…

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of…