arXiv subjects

Wei Cui

Publications and source records attributed to Wei Cui.

At least 91 records · Page 5Linked to original sources

arXiv2022

Extremely large nonlinear response in crystalline quartz at THz frequencies

We report on the first experimental observation of a very strong nonlinear response in crystalline quartz in the terahertz (THz) frequency region through THz time-domain spectroscopy (THz-TDS). Theoretical modelling is presented and predicts a Kerr coefficient n2 equal to 5.17*10^-14 m^2 W^-1. The time-domain analysis of the measured data shows that with increasing of the THz peak amplitude, the pulse experiences a larger time delay in the sample. As the THz amplitude increases to values higher than 110 kV cm^-1, the growth rate of the delay decreases, indicating a saturation process. The value of the nonlinear refractive index calculated through the frequency response analysis is estimated to be on the order of 10^-13 m^2 W^-1, which is several orders of magnitude larger than typical values of the nonlinear refractive index of solids in the visible region. Furthermore, a negative fifth-order susceptibility on the order of 10^-30 m^4 V^-4 is measured.

physics.optics↗

arXiv2022

Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds

Generalized Complete Intersection Calabi-Yau Manifold (gCICY) is a new construction of Calabi-Yau manifolds established recently. However, the generation of new gCICYs using standard algebraic method is very laborious. Due to this complexity, the number of gCICYs and their classification still remain unknown. In this paper, we try to make some progress in this direction using neural network. The results showed that our trained models can have a high precision on the existing type $(1,1)$ and type $(2,1)$ gCICYs in the literature. Moreover, They can achieve a $97\%$ precision in predicting new gCICY which is generated differently from those used for training and testing. This shows that machine learning could be an effective method to classify and generate new gCICY.

hep-th↗

arXiv2022

Metamaterial-based octave-wide terahertz bandpass filters

We present octave-wide bandpass filters in the terahertz (THz) region based on bilayer-metamaterial (BLMM) structures. The passband region has a super-Gaussian shape with a maximum transmittance approaching 70% and a typical stopband rejection of 20 dB. The design is based on a metasurface consisting of a metallic square-hole array deposited on a transparent polymer, which is stacked on top of an identical metasurface with a sub-wavelength separation. The superimposed metasurface structures were designed using finite-difference time-domain (FDTD) simulations and fabricated using a photolithography process. Experimental characterization of these structures between 0.3 to 5.8 THz is performed with a time-domain THz spectroscopy system. Good agreement between experiment and simulation results is observed. We also demonstrate that two superimposed BLMM (2BLMM) devices increase the steepness of the roll-offs to more than 85 dB/octave and enable a superior stopband rejection approaching 40 dB while the maximum transmittance remains above 64%. This work paves the way toward new THz applications, including the detection of THz pulses centered at specific frequencies, and an enhanced time-resolved detection sensitivity towards molecular vibrations that are noise dominated by a strong, off-resonant, driving field.

physics.optics↗

arXiv2022

Single-pulse time-resolved terahertz spectroscopy of sub-millisecond time dynamics

Slow motion movies are not only fascinating to watch, they also allow us to see intricate details of the mechanical dynamics of complex phenomena. If the images in each frame are replaced by terahertz (THz) waves, such movies can monitor low-energy resonances and reveal fast structural or chemical transitions. Here, we combine THz spectroscopy as a non-invasive optical probe with a real-time monitoring technique to resolve non-reproducible phenomena at 50k frames per second. The concept, based on dispersive Fourier transform spectroscopy to achieve unprecedented acquisition speed of THz spectroscopy data, is demonstrated by monitoring sub-millisecond dynamics of hot carriers injected in silicon by successive resonant pulses as a saturation density is established. We anticipate that our experimental configuration will play a crucial role in revealing fast irreversible physical and chemical processes at THz frequencies with microsecond resolution to enable new applications in fundamental research as well as in industry, notably as a rapid supply chain monitoring system in high-volume manufacturing.

physics.optics↗

arXiv2022

Mock HUBS observations of hot gas with IllustrisTNG

The lack of adequate X-ray observing capability is seriously impeding the progress in understanding the hot phase of circumgalactic medium (CGM), which is predicted to extend to the virial radius of a galaxy or beyond, and thus in acquiring key boundary conditions for studying galaxy evolution. To this end, the Hot Universe Baryon Surveyor (\textit{HUBS}) is proposed. \textit{HUBS} is designed to probe hot CGM by detecting its emission or absorption lines with a non-dispersive X-ray spectrometer of high resolution and high throughput. The spectrometer consists of a $60\times60$ array of microcalorimeters, with each detector providing an energy resolution of $2~\mathrm{eV}$, and is placed in the focal plane of an X-ray telescope of $1^{\circ}$ field-of-view. With such a design, the spectrometer is also expected to enable studies of intra-group medium (IGrM) and the outer region of intra-cluster medium (ICM). To assess the scientific potential of \textit{HUBS}, we created mock observations of galaxies, groups, and clusters at different redshifts with the \tng simulation. Focusing exclusively on emission studies in this work, we took into account the effects of light cone, Galactic foreground emission, and background AGN contribution in the mock observations. From the observations, we made mock X-ray images and spectra, analyzed them to derive the properties of the emitting gas in each case, and compared the results with the input parameters from the simulation. The results show that \textit{HUBS} is well suited for studying hot CGM at low redshifts. The redshift range is significantly extended for measuring IGrM and ICM.

astro-ph.GA↗

arXiv2022

Quantum-Inspired Solvers on Mixed-Integer Linear Programming Problem

Mixed-integer linear programming (MILP) plays a crucial role in artificial intelligence, biochemistry, finance, cryptography, etc. Notwithstanding popular for decades, the researches of MILP solvers are still limited by the resource consumption caused by complexity and failure of Moore's Law. Quantum-inspired Ising machines, as a new computing paradigm, can be used to solve integer programming problems by reducing them into Ising models. Therefore, it is necessary to understand the technical evolution of quantum inspired solvers to break the bottleneck. In this paper, the concept and traditional algorithms for MILP are introduced. Then, focused on Ising model, the principle and implementations of annealers and coherent Ising machines are summarized. Finally, the paper discusses the challenges and opportunities of miniaturized solvers in the future.

quant-ph↗

arXiv2022

Active Sensing for Communications by Learning

This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform a sensing or actuation task for maximizing some utility function. In such an active learning setting, the agent needs to design an adaptive sensing strategy sequentially based on the observations made so far. To tackle such a challenging problem in which the dimension of historical observations increases over time, we propose to use a long short-term memory (LSTM) network to exploit the temporal correlations in the sequence of observations and to map each observation to a fixed-size state information vector. We then use a deep neural network (DNN) to map the LSTM state at each time frame to the design of the next measurement step. Finally, we employ another DNN to map the final LSTM state to the desired solution. We investigate the performance of the proposed framework for adaptive channel sensing problems in wireless communications. In particular, we consider the adaptive beamforming problem for mmWave beam alignment and the adaptive reconfigurable intelligent surface sensing problem for reflection alignment. Numerical results demonstrate that the proposed deep active sensing strategy outperforms the existing adaptive or nonadaptive sensing schemes.

cs.IT↗

arXiv2022

Time-Data Tradeoffs in Structured Signals Recovery via the Proximal-Gradient Homotopy Method

In this paper, we characterize data-time tradeoffs of the proximal-gradient homotopy method used for solving linear inverse problems under sub-Gaussian measurements. Our results are sharp up to an absolute constant factor. We demonstrate that, in the absence of the strong convexity assumption, the proximal-gradient homotopy update can achieve a linear rate of convergence when the number of measurements is sufficiently large. Numerical simulations are provided to verify our theoretical results.

cs.IT↗

arXiv2022

A Sharp Algorithmic Analysis of Covariate Adjusted Precision Matrix Estimation with General Structural Priors

In this paper, we present a sharp analysis for a class of alternating projected gradient descent algorithms which are used to solve the covariate adjusted precision matrix estimation problem in the high-dimensional setting. We demonstrate that these algorithms not only enjoy a linear rate of convergence in the absence of convexity, but also attain the optimal statistical rate (i.e., minimax rate). By introducing the generic chaining, our analysis removes the impractical resampling assumption used in the previous work. Moreover, our results also reveal a time-data tradeoff in this covariate adjusted precision matrix estimation problem. Numerical experiments are provided to verify our theoretical results.

cs.IT↗

arXiv2022

Efficient Quantum Feature Extraction for CNN-based Learning

Recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as general function approximators. In this work, we propose a quantum-classical deep network structure to enhance classical CNN model discriminability. The convolutional layer uses linear filters to scan the input data. Moreover, we build PQC, which is a more potent function approximator, with more complex structures to capture the features within the receptive field. The feature maps are obtained by sliding the PQCs over the input in a similar way as CNN. We also give a training algorithm for the proposed model. The hybrid models used in our design are validated by numerical simulation. We demonstrate the reasonable classification performances on MNIST and we compare the performances with models in different settings. The results disclose that the model with ansatz in high expressibility achieves lower cost and higher accuracy.

quant-ph↗

arXiv2021

Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer

This paper proposes a novel scalable reinforcement learning approach for simultaneous routing and spectrum access in wireless ad-hoc networks. In most previous works on reinforcement learning for network optimization, the network topology is assumed to be fixed, and a different agent is trained for each transmission node -- this limits scalability and generalizability. Further, routing and spectrum access are typically treated as separate tasks. Moreover, the optimization objective is usually a cumulative metric along the route, e.g., number of hops or delay. In this paper, we account for the physical-layer signal-to-interference-plus-noise ratio (SINR) in a wireless network and further show that bottleneck objective such as the minimum SINR along the route can also be optimized effectively using reinforcement learning. Specifically, we propose a scalable approach in which a single agent is associated with each flow and makes routing and spectrum access decisions as it moves along the frontier nodes. The agent is trained according to the physical-layer characteristics of the environment using a novel rewarding scheme based on the Monte Carlo estimation of the future bottleneck SINR. It learns to avoid interference by intelligently making joint routing and spectrum allocation decisions based on the geographical location information of the neighbouring nodes.

eess.SP↗

arXiv2021

Bayesian spectral density approach for identification and uncertainty quantification of bridge section's flutter derivatives operated in turbulent flow

This study presents a Bayesian spectral density approach for identification and uncertainty quantification of flutter derivatives of bridge sections utilizing buffeting displacement responses, where the wind tunnel test is conducted in turbulent flow. Different from traditional time-domain approaches (e.g., least square method and stochastic subspace identification), the newly-proposed approach is operated in frequency domain. Based on the affine invariant ensemble sampler algorithm, Markov chain Monte-Carlo sampling is employed to accomplish the Bayesian inference. The probability density function of flutter derivatives is modeled based on complex Wishart distribution, where probability serves as the measure. By the Bayesian spectral density approach, the most probable values and corresponding posterior distributions (namely identification uncertainty here) of each flutter derivative can be obtained at the same time. Firstly, numerical simulations are conducted and the identified results are accurate. Secondly, thin plate model, flutter derivatives of which have theoretical solutions, is chosen to be tested in turbulent flow for the sake of verification. The identified results of thin plate model are consistent with the theoretical solutions. Thirdly, the center-slotted girder model, which is widely-utilized long-span bridge sections in current engineering practice, is employed to investigate the applicability of the proposed approach on a general bridge section. For the center-slotted girder model, the flutter derivatives are also extracted by least square method in uniform flow to cross validate the newly-proposed approach. The identified results by two different approaches are compatible.

stat.AP↗

arXiv2021

Algorithmic Decision Making with Conditional Fairness

Nowadays fairness issues have raised great concerns in decision-making systems. Various fairness notions have been proposed to measure the degree to which an algorithm is unfair. In practice, there frequently exist a certain set of variables we term as fair variables, which are pre-decision covariates such as users' choices. The effects of fair variables are irrelevant in assessing the fairness of the decision support algorithm. We thus define conditional fairness as a more sound fairness metric by conditioning on the fairness variables. Given different prior knowledge of fair variables, we demonstrate that traditional fairness notations, such as demographic parity and equalized odds, are special cases of our conditional fairness notations. Moreover, we propose a Derivable Conditional Fairness Regularizer (DCFR), which can be integrated into any decision-making model, to track the trade-off between precision and fairness of algorithmic decision making. Specifically, an adversarial representation based conditional independence loss is proposed in our DCFR to measure the degree of unfairness. With extensive experiments on three real-world datasets, we demonstrate the advantages of our conditional fairness notation and DCFR.

cs.LG↗

arXiv2021

Anonymous communication protocol over quantum networks

We propose a W state-based protocol for anonymously transmitting quantum messages in a quantum network. Different from the existing protocols [A. Unnikrishnan, et al., Phys. Rev. Lett. 122, 240501 (2019)], the proposed protocol can be effectively implemented in the network only equipped with quantum channels and regular broadcast channels. Throughout the design procedure, we develop three sub-protocols using the W state, including the quantum collision detection protocol and the quantum notification protocol. Moreover, together with the conventional anonymous entanglement protocol, the whole anonymous communication protocol has been constructed. Finally, we examine the correctness and security of the proposed quantum anonymous communication protocol.

quant-ph↗

arXiv2021

Module for arbitrary controlled rotation in gate-based quantum algorithms

To assess whether a gate-based quantum algorithm can be executed successfully on a noisy intermediate-scale quantum (NISQ) device, both complexity and actual value of quantum resources should be considered carefully. Based on quantum phase estimation, we implemente arbitrary controlled rotation of quantum algorithms with a proposed modular method. The proposed method is not limited to be used as a submodule of the HHL algorithm and can be applied to more general quantum machine learning algorithms. Compared with the polynomial-fitting function method, our method only requires the least ancillas and the least quantum gates to maintain the high fidelity of quantum algorithms. The method theoretically will not influence the acceleration of original algorithms. Numerical simulations illustrate the effectiveness of the proposed method. Furthermore, if the corresponding diagonal unitary matrix can be effectively decomposed, the method is also polynomial in time cost.

quant-ph↗

arXiv2021

An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection

Combining the advantages of quantum computing and neural networks, quantum neural networks (QNNs) have gained considerable attention recently. However, because of the lack of quantum resource, it is costly to train QNNs. In this work, we presented a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. Each level of the resulting feature extractors consist of multiple quanvolution filters, followed by a pooling layer. The main contribution of the proposed approach is to use the $K$-means clustering to maximize the difference of quantum properties in quantum circuit ansatz. One experiment on the bearing fault detection task shows the effectiveness of the proposed method.

quant-ph↗

arXiv2021

Hybrid algorithms to solve linear systems of equations with limited qubit resources

The solution of linear systems of equations is a very frequent operation and thus important in many fields. The complexity using classical methods increases linearly with the size of equations. The HHL algorithm proposed by Harrow et al. achieves exponential acceleration compared with the best classical algorithm. However, it has a relatively high demand for qubit resources and the solution $\left| x \right\rangle $ is in a normalized form. Assuming that the eigenvalues of the coefficient matrix of the linear systems of equations can be represented perfectly by finite binary number strings, three hybrid iterative phase estimation algorithms (HIPEA) are designed based on the iterative phase estimation algorithm in this paper. The complexity is transferred to the measurement operation in an iterative way, and thus the demand of qubit resources is reduced in our hybrid algorithms. Moreover, the solution is stored in a classical register instead of a quantum register, so the exact unnormalized solution can be obtained. The required qubit resources in the three HIPEA algorithms are different. HIPEA-1 only needs one single ancillary qubit. The number of ancillary qubits in HIPEA-2 is equal to the number of nondegenerate eigenvalues of the coefficient matrix of linear systems of equations. HIPEA-3 is designed with a flexible number of ancillary qubits. The HIPEA algorithms proposed in this paper broadens the application range of quantum computation in solving linear systems of equations by avoiding the problem that quantum programs may not be used to solve linear systems of equations due to the lack of qubit resources.

quant-ph↗

arXiv2021

Life-cycle assessment for flutter probability of a long-span suspension bridge based on field monitoring data

Assessment of structural safety status is of paramount importance for existing bridges, where accurate evaluation of flutter probability is essential for long-span bridges. In current engineering practice, at the design stage, flutter critical wind speed is usually estimated by the wind tunnel test, which is sensitive to modal frequencies and damping ratios. After construction, structural properties of existing structures will change with time due to various factors, such as structural deteriorations and periodic environments. The structural dynamic properties, such as modal frequencies and damping ratios, cannot be considered as the same values as the initial ones, and the deteriorations should be included when estimating the life-cycle flutter probability. This paper proposes an evaluation framework to assess the life-cycle flutter probability of long-span bridges considering the deteriorations of structural properties, based on field monitoring data. The Bayesian approach is employed for modal identification of a suspension bridge with the main span of 1650 m, and the field monitoring data during 2010-2015 is analyzed to determine the deterioration functions of modal frequencies and damping ratios, as well as their inter-seasonal fluctuations. According to the historical trend, the long-term structural properties can be predicted, and the probability distributions of flutter critical wind speed for each year in the long term are calculated. Consequently, the life-cycle flutter probability is estimated, based on the predicted modal frequencies and damping ratios.

stat.CO↗