arXiv subjects

Wenqi Zhu

Publications and source records attributed to Wenqi Zhu.

34 records · Page 2Linked to original sources

arXiv2024

Laser cooling $^{88}$Sr to microkelvin temperature with an integrated-photonics system

We report on experiments generating a magneto-optical trap (MOT) of 88-strontium ($^{88}$Sr) atoms at microkelvin temperature, using integrated-photonics devices. With metasurface optics integrated on a fused-silica substrate, we generate six-beam, circularly polarized, counter-propagating MOTs on the blue broad-line, 461 nm, and red narrow-line, 689 nm, Sr cooling transitions without bulk optics. By use of a diverging beam configuration, we create up to 10 mm diameter MOT beams at the trapping location. To frequency stabilize and linewidth narrow the cooling lasers, we use fiber-packaged, integrated nonlinear waveguides to spectrally broaden a frequency comb. The ultra-coherent supercontinuum of the waveguides covers 650 nm to 2500 nm, enabling phase locks of the cooling lasers to hertz level linewidth. Our work highlights the possibility to simplify the preparation of an ultracold 88Sr gas for an optical-lattice clock with photonic devices. By implementing a timing sequence for control of the MOT lasers and the quadrupole magnetic-field gradient, we collect atoms directly from a thermal beam into the blue MOT and continuously cool into a red MOT with dynamic detuning and intensity control. There, the red MOT temperature is as low as $2~μ$K and the overall transfer efficiency up to 16%. We characterize this sequence, including an intermediate red MOT with modulated detuning. Our experiments demonstrate an integrated photonics system capable of cooling alkaline-earth gases to microkelvin temperature with sufficient transfer efficiencies for adoption in scalable optical clocks and quantum sensors.

physics.atom-ph↗

arXiv2024

Global Convergence of High-Order Regularization Methods with Sums-of-Squares Taylor Models

High-order tensor methods that employ Taylor-based local models (of degree $p\ge 3$) within adaptive regularization frameworks have been recently proposed for both convex and nonconvex optimization problems. They have been shown to have superior, and even optimal, worst-case global convergence rates and local rates compared to Newton's method. Finding rigorous and efficient techniques for minimizing the Taylor polynomial sub-problems remains a challenging aspect for these algorithms. Ahmadi et al. recently introduced a tensor method based on sum-of-squares (SoS) reformulations, so that each Taylor polynomial sub-problem in their approach can be tractably minimized using semidefinite programming (SDP); however, the global convergence and complexity of their method have not been addressed for general nonconvex problems. This paper introduces an algorithmic framework that combines the Sum of Squares (SoS) Taylor model with adaptive regularization techniques for nonconvex smooth optimization problems. Each iteration minimizes an SoS Taylor model, offering a polynomial cost per iteration. For general nonconvex functions, the worst-case evaluation complexity bound is $\mathcal{O}(ε^{-2})$, while for strongly convex functions, an improved evaluation complexity bound of $\mathcal{O}(ε^{-\frac{1}{p}})$ is established. To the best of our knowledge, this is the first global rate analysis for an adaptive regularization algorithm with a tractable high-order sub-problem in nonconvex smooth optimization, opening the way for further improvements.

math.OC↗

arXiv2024

Three-dimensional, multi-wavelength beam formation with integrated metasurface optics for Sr laser cooling

We demonstrate the formation of a complex, multi-wavelength, three-dimensional laser beam configuration with integrated metasurface optics. Our experiments support the development of a compact Sr optical-lattice clock, which leverages magneto-optical trapping on atomic transitions at 461 nm and 689 nm without bulk free-space optics. We integrate six, mm-scale metasurface optics on a fused-silica substrate and illuminate them with light from optical fibers. The metasurface optics provide full control of beam pointing, divergence, and polarization to create the laser configuration for a magneto-optical trap. We report the efficiency and integration of the three-dimensional visible laser beam configuration, demonstrating the suitability of metasurface optics for atomic laser cooling.

physics.optics↗

arXiv2023

Cubic-quartic regularization models for solving polynomial subproblems in third-order tensor methods

High-order tensor methods for solving both convex and nonconvex optimization problems have generated significant research interest, leading to algorithms with optimal global rates of convergence and local rates that are faster than Newton's method. On each iteration, these methods require the unconstrained local minimization of a (potentially nonconvex) multivariate polynomial of degree higher than two, constructed using third-order (or higher) derivative information, and regularized by an appropriate power of regularization. Developing efficient techniques for solving such subproblems is an ongoing topic of research, and this paper addresses the case of the third-order tensor subproblem. We propose the CQR algorithmic framework, for minimizing a nonconvex Cubic multivariate polynomial with Quartic Regularisation, by minimizing a sequence of local quadratic models that incorporate simple cubic and quartic terms. The role of the cubic term is to crudely approximate local tensor information, while the quartic one controls model regularization and progress. We provide necessary and sufficient optimality conditions that fully characterise the global minimizers of these cubic-quartic models. We then turn these conditions into secular equations that can be solved using nonlinear eigenvalue techniques. We show, using our optimality characterisations, that a CQR algorithmic variant has the optimal-order evaluation complexity of $\mathcal{O}(ε^{-3/2})$ when applied to minimizing our quartically-regularised cubic subproblem, which can be further improved in special cases. We propose practical CQR variants that use local tensor information to construct the local cubic-quartic models. We test these variants numerically and observe them to be competitive with ARC and other subproblem solvers on typical instances and even superior on ill-conditioned subproblems with special structure.

math.OC↗

arXiv2023

Newsvendor Conditional Value-at-Risk Minimisation: a Feature-based Approach under Adaptive Data Selection

The classical risk-neutral newsvendor problem is to decide the order quantity that maximises the expected profit. Some recent works have proposed an alternative model, in which the goal is to minimise the conditional value-at-risk (CVaR), a different but very much important risk measure in financial risk management. In this paper, we propose a feature-based non-parametric approach to Newsvendor CVaR minimisation under adaptive data selection (NPC). The NPC method is simple and general. It can handle minimisation with both linear and nonlinear profits, and requires no prior knowledge of the demand distribution. Our main contribution is two-fold. Firstly, NPC uses a feature-based approach. The estimated parameters of NPC can be easily applied to prescriptive analytic to provide additional operational insights. Secondly, unlike common non-parametric methods, our NPC method uses an adaptive data selection criterion and requires only a small proportion of data (only data from two tails), significantly reducing the computational effort. Results from both numerical and real-life experiments confirm that NPC is robust with regard to difficult and large data structures. Using fewer data points, the computed order quantities from NPC lead to equal or less downside loss in extreme cases than competing methods.

math.OC↗

arXiv2022

Single atom trapping in a metasurface lens optical tweezer

Optical metasurfaces of subwavelength pillars have provided new capabilities for the versatile definition of the amplitude, phase, and polarization of light. In this work, we demonstrate that an efficient dielectric metasurface lens can be used to trap and image single neutral atoms with a long working distance from the lens of 3 mm. We characterize the high-numerical-aperture optical tweezers using the trapped atoms and compare with numerical computations of the metasurface lens performance. We predict that future metasurfaces for atom trapping will be able to leverage multiple ongoing developments in metasurface design and enable multifunctional control in complex quantum information experiments with neutral-atoms arrays.

physics.atom-ph↗

arXiv2022

Roadmap on spatiotemporal light fields

Spatiotemporal sculpturing of light pulse with ultimately sophisticated structures represents the holy grail of the human everlasting pursue of ultrafast information transmission and processing as well as ultra-intense energy concentration and extraction. It also holds the key to unlock new extraordinary fundamental physical effects. Traditionally, spatiotemporal light pulses are always treated as spatiotemporally separable wave packet as solution of the Maxwell's equations. In the past decade, however, more generalized forms of spatiotemporally nonseparable solution started to emerge with growing importance for their striking physical effects. This roadmap intends to highlight the recent advances in the creation and control of increasingly complex spatiotemporally sculptured pulses, from spatiotemporally separable to complex nonseparable states, with diverse geometric and topological structures, presenting a bird's eye viewpoint on the zoology of spatiotemporal light fields and the outlook of future trends and open challenges.

physics.optics↗

arXiv2022

Dynamical commensuration effect in a two-dimensional Yukawa solid modulated by periodic substrates

Transverse depinning dynamics of a periodic-square-substrate modulated two-dimensional dusty plasma solid driven by a constant force in the longitudinal direction are investigated using Langevin dynamical simulations. During the increase of the commensuration ratio (the number ratio of particles to substrate potential wells), the nonmonotonic variation trend of the critical transverse depinning force is observed. It is found that the local maxima and minima of the critical transverse depinning force just correspond to the dynamical commensurate and incommensurate conditions, respectively. The dynamical commensurate effect is also clearly exhibited from the stable one-dimensional channel particle trajectories and the highly ordered structure, however, both the particle trajectories and the structure are more disordered under the incommensurate conditions. The nonmonotonic variation of the critical transverse depinning force is attributed to the stability of the lattice structure under various commensuration ratios.

physics.plasm-ph↗

arXiv2022

Highly-twisted states of light from a high quality factor photonic crystal ring

Twisted light with orbital angular momentum (OAM) has been extensively studied for applications in quantum and classical communications, microscopy, and optical micromanipulation. Ejecting the naturally high angular momentum whispering gallery modes (WGMs) of an optical microresonator through a grating-assisted mechanism, where the generated OAM number ($l$) is the difference of the angular momentum of the WGM and that of the grating, provides a scalable, chip-integrated solution for OAM generation. However, demonstrated OAM microresonators have exhibited a much lower quality factor ($Q$) than conventional WGM resonators (by $>100\times$), and an understanding of the ultimate limits on $Q$ has been lacking. This is crucial given the importance of $Q$ in enhancing light-matter interactions, such as single emitter coupling and parametric nonlinear processes, that underpin many important microresonator applications. Moreover, though high-OAM states are often desirable, the limits on what is achievable in a microresonator configuration are not well understood. Here, we provide new physical insight on these two longstanding questions, through understanding OAM from the perspective of mode coupling in a photonic crystal ring, and linking it to the commonly studied case of coherent backscattering between counter-propagating WGMs. In addition to demonstrating high-$Q$ ($10^5$ to $10^6$), high estimated OAM ejection efficiency (up to $90~\%$), and high-OAM number (up to $l$ = 60), our empirical model is supported by experiments and provides a quantitative explanation for the behavior of $Q$ and OAM ejection efficiency with $l$ for the first time. The state-of-the-art performance and new understanding of the physics of microresonator OAM generation will open new opportunities for realizing OAM applications using chip-integrated technologies.

physics.optics↗

arXiv2022

Directional Locking in a 2D Yukawa Solid Modulated by a 2D Periodic Substrate

Directional depinning dynamics of a 2D dusty plasma solid modulated by a 2D square periodic substrate are investigated using Langevin dynamical simulations. We observe prominent directional locking effects when the direction of the external driving force is varied relative to the underlying square substrate. These locking steps appear when the direction of the driving force is close to the symmetry direction of the substrate, corresponding to the different dynamical flow patterns and the structures. In the conditions between the adjacent locking steps, moving ordered states are observed. Although the discontinuous transitions often occur between the locking steps and the non-locking portion, the continuous transitions are also found around the locking step associated with the disordered plastic flow close to its termini. Our results show that directional locking also occurs for underdamped systems, which could be tested experimentally in dusty plasmas modulated by 2D substrates.

physics.plasm-ph↗

arXiv2019

Sparse-Dense Subspace Clustering

Subspace clustering refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Current subspace clustering approaches are usually based on a two-stage framework. In the first stage, an affinity matrix is generated from data. In the second one, spectral clustering is applied on the affinity matrix. However, the affinity matrix produced by two-stage methods cannot fully reveal the similarity between data points from the same subspace (intra-subspace similarity), resulting in inaccurate clustering. Besides, most approaches fail to solve large-scale clustering problems due to poor efficiency. In this paper, we first propose a new scalable sparse method called Iterative Maximum Correlation (IMC) to learn the affinity matrix from data. Then we develop Piecewise Correlation Estimation (PCE) to densify the intra-subspace similarity produced by IMC. Finally we extend our work into a Sparse-Dense Subspace Clustering (SDSC) framework with a dense stage to optimize the affinity matrix for two-stage methods. We show that IMC is efficient when clustering large-scale data, and PCE ensures better performance for IMC. We show the universality of our SDSC framework as well. Experiments on several data sets demonstrate the effectiveness of our approaches. Moreover, we are the first one to apply densification on affinity matrix before spectral clustering, and SDSC constitutes the first attempt to build a universal three-stage subspace clustering framework.

cs.LG↗

arXiv2019

Residual Encoder-Decoder Network for Deep Subspace Clustering

Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to solve subspace clustering problems. However, the training of current deep methods converges slowly, which is much less efficient than traditional approaches. We propose a Residual Encoder-Decoder network for deep Subspace Clustering (RED-SC), which symmetrically links convolutional and deconvolutional layers with skip-layer connections, with which the training converges much faster. We use a self-expressive layer to generate more accurate linear representation coefficients through different latent representations from multiple latent spaces. Experiments show the superiority of RED-SC in training efficiency and clustering accuracy. Moreover, we are the first one to apply residual encoder-decoder on unsupervised learning tasks.

cs.LG↗

arXiv2019

Three-Stage Subspace Clustering Framework with Graph-Based Transformation and Optimization

Subspace clustering (SC) refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Based on spectral clustering, state-of-the-art approaches solve SC problem within a two-stage framework. In the first stage, data representation techniques are applied to draw an affinity matrix from the original data. In the second stage, spectral clustering is directly applied to the affinity matrix so that data can be grouped into different subspaces. However, the affinity matrix obtained in the first stage usually fails to reveal the authentic relationship between data points, which leads to inaccurate clustering results. In this paper, we propose a universal Three-Stage Subspace Clustering framework (3S-SC). Graph-Based Transformation and Optimization (GBTO) is added between data representation and spectral clustering. The affinity matrix is obtained in the first stage, then it goes through the second stage, where the proposed GBTO is applied to generate a reconstructed affinity matrix with more authentic similarity between data points. Spectral clustering is applied after GBTO, which is the third stage. We verify our 3S-SC framework with GBTO through theoretical analysis. Experiments on both synthetic data and the real-world data sets of handwritten digits and human faces demonstrate the universality of the proposed 3S-SC framework in improving the connectivity and accuracy of SC methods based on $\ell_0$, $\ell_1$, $\ell_2$ or nuclear norm regularization.

cs.LG↗

arXiv2019

Restricted Connection Orthogonal Matching Pursuit For Sparse Subspace Clustering

Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time.

cs.LG↗

arXiv2019

Revisiting the Photon-Drag Effect in Metal Films

The photon-drag effect, the rectified current in a medium induced by conservation of momentum of absorbed or redirected light, is a unique probe of the detailed mechanisms underlying radiation pressure. We revisit this effect in gold, a canonical Drude metal. We discover that the signal for p-polarized illumination in ambient air is affected in both sign and magnitude by adsorbed molecules, opening previous measurements for reinterpretation. Further, we show that the intrinsic sign of the photon-drag effect is contrary to the prevailing intuitive model of direct momentum transfer to free electrons.

physics.optics↗

arXiv2016

Surface-Plasmon-Polariton Laser based on an Open-Cavity Fabry-Perot Resonator

Recent years have witnessed growing interest in the development of small-footprint lasers for potential applications in small-volume sensing and on-chip optical communications. Surface-plasmons, electromagnetic modes evanescently confined to metal-dielectric interfaces, offer an effective route to achieving lasing at nanometer-scale dimensions when resonantly amplified in contact with a gain-medium. Here, we achieve visible frequency ultra-narrow linewidth lasing at room-temperature by leveraging surface plasmons propagating in an open Fabry-Perot cavity formed by a flat metal surface coated with a subwavelength-thick layer of optically-pumped gain medium and orthogonally bound by a pair of flat metal sidewalls. Low perturbation transmission-configuration sampling of the lasing plasmon mode is achieved via an evanescently coupled recessed nanoslit, opening the way to high-figure-of-merit refractive-index sensing of analytes interacting with the open cavity.

physics.optics↗