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This article presents a detailed analysis, based on the first-principles finite-difference time-domain method, of the resonant frequency, quality factor (Q), mode volume (V), and radiation pattern of the fundamental (HE11) mode in a…
This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models. A simple four qubit quantum circuit that uses Y rotation gates for encoding and two controlled NOT…
We report on the spectroscopic investigation of quantum dot - micropillar cavities with unprecedented quality factors. We observe a pronounced dependency of the quality factor on the measurement scheme, and find that significantly larger…
The emergence of dielectric bowtie cavities enable optical confinement with ultrahigh quality factor and ultra-small optical mode volumes with perspectives for enhanced light-matter interaction. Experimental work has so far emphasized the…
Periodic nanoparticle arrays can support surface lattice resonances (SLRs), which arise from the hybridization between localized surface plasmons (LSPs) and diffractive Rayleigh anomalies (RAs). In contrast to LSPs, SLRs enjoy a much higher…
We theoretically demonstrate high Quality factor micropillar cavities with record low mode volumes based on the TiO2/SiO2 material system. The proposed cavities have Q/V three orders of magnitude larger than previously reported ones. We…
Hybrid photonic-plasmonic cavities have emerged as a new platform to increase light-matter interaction capable to enhance the Purcell factor in a singular way not attainable with either photonic or plasmonic cavities separately. In the…
Phononic crystals are a promising platform for the study of quantum acoustodynamics. In a recent experiment, the interaction of a superconducting quantum bit with modes of a phononic crystal has been demonstrated. The field of these modes…
We introduce a numerical procedure which permits to drastically accelerate the design of multimode photonic crystal resonators. Specifically, we demonstrate that the optical response of an important class of such nanoscale structures is…
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision…
Focusing and guiding light into semiconductor nanostructures can deliver revolutionary concepts for photonic devices, which offer a practical pathway towards next-generation power-efficient optical networks. In this review, we consider the…
Polymers have appealing optical, biochemical, and mechanical qualities, including broadband transparency, ease of functionalization, and biocompatibility. However, their low refractive indices have precluded wavelength-scale optical…
Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport…
Computational methods that automatically extract knowledge from data are critical for enabling data-driven materials science. A reliable identification of lattice symmetry is a crucial first step for materials characterization and…
We propose a practical, scalable, and efficient scheme for quantum computation using spatially separated matter qubits and single photon interference effects. The qubit systems can be NV-centers in diamond, Pauli-blockade quantum dots with…
Photonic crystals use periodic structures to create forbidden frequency regions for optical wave propagation, that allow for the creation and integration of complex optical functions in small footprint devices. Such strategy has also been…
The rapid scaling of deep neural networks comes at the cost of unsustainable power consumption. While optical neural networks offer an alternative, their capabilities remain constrained by the lack of efficient optical nonlinearities. To…
A novel method for learning optimal, orthonormal wavelet bases for representing 1- and 2D signals, based on parallels between the wavelet transform and fully connected artificial neural networks, is described. The structural similarities…
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. tuning up devices. In many…
D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may…