arXiv subjects

Qingyi Zhou

Publications and source records attributed to Qingyi Zhou.

11 recordsLinked to original sources

arXiv2026

Full-wave nonlinear microscopy reveals guided channel for ultrafast polariton transport

We show how finite-difference time-domain (FDTD) simulations can be extended to model ultrafast nonlinear microscopy, enabling the prediction of spatially-resolved pump--probe signals in arbitrary electromagnetic environments. Focusing on polariton transport in strongly coupled light--matter systems, we develop a perturbative framework to study the ultrafast propagation of hybrid light--matter excitations in nanophotonic structures. We first apply the framework to a standard distributed Bragg reflector (DBR) cavity, reproducing established results for polariton transport from a multimode Tavis--Cummings model. We then consider the full modal landscape of the same cavity, including guided modes below the light line that are typically neglected in single-mode-family descriptions. Exploiting these modes, we design compact mode converters that transfer radiative cavity polaritons into photon-like guided polaritons and back, utilizing the guided modes for low-loss propagation. Despite molecular dephasing, this enables transport of molecular excitation over a millimeter, an order of magnitude beyond current transport experiments. We further compute the pump--probe differential transmission signal, providing an experimental signature of the mechanism. Our results show that the modal landscape of a photonic cavity can be engineered to bypass limitations commonly assumed to be intrinsic to the transport of molecular polaritons.

quant-ph↗

arXiv2026

Real-Space Imaging of Guided Exciton Polaritons in Free-standing Monolayer WSe2

Monolayers of transition metal dichalcogenides (TMDCs), known for their strong excitonic states with high binding energies in the visible spectrum at room temperature, offer great potential for polariton-driven devices. While polariton guided modes in bulk TMDCs have been reported the real space experimental observation of 2D exciton-polariton guided modes in a monolayer remains challenging due to various mode cut-off conditions that arise as the TMDC layer becomes thinner, including cut-off frequency, mode confinement and boundary conditions. Here using scanning near-field optical microscopy (s-SNOM), we directly visualized the real-space propagation of these guided modes for the first time in an angstrom-thick, suspended monolayer of WSe2. Through numerical simulations we have also validated that the guided mode can only exist in a monolayer WSe2 when symmetric cladding conditions are closely applied. By tuning the excitation laser energy and analysing the guided mode distribution, we observed a pronounced back-bending dispersion around the A exciton, indicating strong light-matter interactions, and confirmed the existence of the fundamental TE0 exciton polariton (EP) propagation mode. The unique dispersion characteristics of these modes were further validated through theoretical modelling of the mode in free-standing monolayer WSe2. Our findings provide crucial experimental evidence of guided mode EPs in atomically thin TMDCs, opening new possibilities for nanoscale photonic applications.

physics.optics↗

arXiv2026

Avalanche Sensing via Kerr frequency comb in an Optical Microcavity

Sensors based on optical microcavities enhance light-matter interactions within an ultraconfined volume, enabling high-sensitivity detection across a wide range of sensing applications. In these systems, environmental perturbations modify the intrinsic resonance properties of the cavity, typically manifested as frequency shifts, linewidth broadening, or mode splitting. However, the minimum resolvable change in these spectral properties fundamentally limits the overall sensor sensitivity. Here, we propose a new avalanche sensing scheme enabled by Kerr nonlinearity. Instead of relying on the detection of frequency shifts, our approach exploits abrupt state transitions in a Kerr frequency comb to amplify weak perturbations. We provide a theoretical analysis of the underlying mechanism of this scheme and validate the concept through both coupled-mode theory (CMT) modeling and full-wave electromagnetic simulations.

physics.optics↗

arXiv2026

Quantum Nonlinearity for Optical Neural Computing

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 address this, we propose an optical neural computing architecture by embedding quantum emitters in inverse-designed nanophotonic structures. Due to their saturability, quantum emitters exhibit exceptionally strong nonlinearity compared with conventional materials. Using physics-aware training, we numerically demonstrate that the proposed architecture can solve complex tasks, including nonlinear classification and reinforcement learning, within all-optical neural networks. To enable fair comparison across different platforms, we introduce a framework that quantitatively links nonlinearity to a network's expressive power. Analysis shows that our quantum activation operates at $\text{nW}/\mu\text{m}^2$ intensity, which is seven orders of magnitude below the nonlinearity threshold of conventional optical materials. Looking ahead to large language models, we estimate the nonlinearity-limited optical power, which scales sublinearly with model size. Our results indicate that quantum nanophotonics may provide a route toward sustainable AI inference.

physics.optics↗

arXiv2025

Efficient simulation framework for modeling collective emission in ensembles of inhomogeneous solid-state emitters

An efficient simulation framework is proposed to model collective emission in disordered ensembles of quantum emitters. Using a cumulant expansion approach, the computational complexity scales polynomially as opposed to exponentially with the number of emitters, enabling Monte Carlo sampling over a large number of realizations. The framework is applied to model negatively charged silicon-vacancy (SiV$^{-}$) centers inside diamond. Incorporating spatial disorder and inhomogeneous broadening, we obtain statistically averaged responses over hundreds of SiV$^{-}$ clusters. These simulations reveal two signatures of collective behavior. First, dynamics of fully inverted clusters show that superradiant emission occurs only with sufficiently large emitter number and high quantum efficiency. Unlike ideal Dicke superradiance, the burst is substantially suppressed by strong near-field dipole-dipole interaction, consistent with existing theoretical predictions. Second, under continuous-wave excitation we compute photoluminescence-excitation spectra, which exhibit interaction-induced broadening in the distribution of resonance peaks. The corresponding density of states also displays a non-zero skewness. Overall, by incorporating realistic inhomogeneities in emitter clusters, our framework is able to predict statistics for disordered ensembles that can be compared to experiments directly. Our approach generalizes to other types of emitters, including atoms, molecules, and quantum dots, thus providing a practical tool for analyzing collective behavior in realistic quantum systems.

physics.optics↗

arXiv2025

Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning

Non-contact manipulation has emerged as a transformative approach across various industrial fields. However, current flexible 2D and 3D non-contact manipulation techniques are often limited to microscopic scales, typically controlling objects in the milligram range. In this paper, we present a magnetic levitation system, termed Maglev-Pentabot, designed to address this limitation. The Maglev-Pentabot leverages deep reinforcement learning (DRL) to develop complex control strategies for manipulating objects in the gram range. Specifically, we propose an electromagnet arrangement optimized through numerical analysis to maximize controllable space. Additionally, an action remapping method is introduced to address sample sparsity issues caused by the strong nonlinearity in magnetic field intensity, hence allowing the DRL controller to converge. Experimental results demonstrate flexible manipulation capabilities, and notably, our system can generalize to transport tasks it has not been explicitly trained for. Furthermore, our approach can be scaled to manipulate heavier objects using larger electromagnets, offering a reference framework for industrial-scale robotic applications.

cs.RO↗

arXiv2024

Photonic systolic array for all-optical matrix-matrix multiplication

Systolic arrays have proven to be highly efficient for parallelized matrix-matrix multiplication (MMM), utilizing synchronized, heartbeat-like data flows across an array of processing elements. While optical structures such as waveguide crossbar arrays and Mach-Zehnder interferometer-based meshes serve as photonic equivalents to the systolic arrays, the disparity between the two input matrices for multiplication -- one using optical signals and the other with system-defined parameters -- gives rise to a bottleneck in modern machine-learning tasks, such as evaluating attention scores in large language models. Here, we propose a photonic systolic array that performs MMM entirely with optical signals, utilizing homodyne detection at each array cell. Adjoint-based design of compact on-chip freeform optical modules enables precise control of light flow without bulky waveguide coupling schemes. The operation of a $4\times4$ photonic systolic array is numerically verified, achieving a theoretical computation density of $6.2~\mathrm{PMACs}/\mathrm{mm}^2/\mathrm{s}$. This design marks a significant step toward practical photonic computing hardware for modern AI workloads.

physics.optics↗

arXiv2024

Simulating quantum emitters in arbitrary photonic environments using FDTD: beyond the semi-classical regime

We propose a numerical algorithm that integrates quantum two-level systems (TLSs) into the finite-difference time-domain (FDTD) framework for simulating quantum emitters in arbitrary 3D photonic environments. Conventional methods struggle with these systems due to their semi-classical nature and spurious self-interactions that arise when a TLS is driven by its own radiation field. We address these issues by determining the correct electric field for driving the TLS, as well as the current source used in FDTD for modeling photon emission. Our method, focusing on single-excitation states, employs a total field-incident field (TF-IF) technique to eliminate self-interactions, enabling precise simulations of photon emission and scattering. The algorithm also successfully models complex phenomena such as resonant energy transfer, superradiance, and vacuum Rabi splitting. This powerful computational tool is expected to substantially advance research in nanophotonics, quantum physics, and beyond.

quant-ph↗

arXiv2024

Long-Propagating Ghost Phonon Polaritons Enabled by Selective Mode Excitation

The precise control of phonon polaritons(PhPs) is essential for advancements in nanophotonic applications like on-chip optical communication and quantum information processing. Ghost hyperbolic phonon polaritons (g-HPs), which have been recently discovered, feature in-plane hyperbolic dispersion and oblique wavefronts, enabling long-range propagation. Despite their potential, controlling the directionality and selective excitation of g-HPs remains challenging. Our research demonstrates that modifying the shape of the launching micro/nano antenna can achieve this control. Using an asymmetric triangular gold antenna on a calcite crystal surface, we achieve highly directional g-HP excitation by selectively targeting specific polariton modes. Additionally, the mode of g-HPs can be adjusted by changing the excitation wavelength or rotating the antenna. Remarkably, our near-field imaging experiments show g-HP propagation over distances exceeding 35 micrometers, more than twice the length reported in previous studies. This work merges g-HP theory with structural engineering, enhancing the control over g-HPs and paving the way for innovative applications in mid-IR optoelectronics.

physics.optics↗

arXiv2022

Sparsity for Ultrafast Material Identification

Mid-infrared spectroscopy is often used to identify material. Thousands of spectral points are measured in a time-consuming process using expensive table-top instrument. However, material identification is a sparse problem, which in theory could be solved with just a few measurements. Here we exploit the sparsity of the problem and develop an ultra-fast, portable, and inexpensive method to identify materials. In a single-shot, a mid-infrared camera can identify materials based on their spectroscopic signatures. This method does not require prior calibration, making it robust and versatile in handling a broad range of materials.

physics.ins-det↗

arXiv2020

AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning

The demand for high speed data transmission has increased rapidly, leading to advanced optical communication techniques. In the past few years, multiple equalizers based on neural network (NN) have been proposed to recover signal from nonlinear distortions. However, previous experiments mainly focused on achieving low bit error rate (BER) on certain dataset with an offline-trained NN, neglecting the generalization ability of NN-based equalizer when the properties of optical link change. The development of efficient online training scheme is urgently needed. In this paper, we've proposed an adaptive online training scheme, which can fine-tune parameters of NN-based equalizer without the help of an online training sequence. By introducing data augmentation and virtual adversarial training, the convergence speed has been accelerated by 4.5 times, compared with decision-directed self-training. The proposed adaptive NN-based equalizer is called "AdaNN". Its BER has been evaluated under two scenarios: a 56 Gb/s PAM4-modulated VCSEL-MMF optical link (100-m), and a 32 Gbaud 16QAM-modulated Nyquist-WDM system (960-km SSMF). In our experiments, with the help of AdaNN, BER values can be quickly stabilized below 1e-3 after trained with 10^5 unlabeled symbols. AdaNN shows great performance improvement compared with non-adaptive NN and conventional MLSE.

eess.SP↗