arXiv subjects

Ying Li

Publications and source records attributed to Ying Li.

At least 307 records · Page 17Linked to original sources

arXiv2023

Maximising Quantum-Computing Expressive Power through Randomised Circuits

In the noisy intermediate-scale quantum era, variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. However, the success of VQAs depends on the expressive power of parameterised quantum circuits, which is constrained by the limited gate number and the presence of barren plateaus. In this work, we propose and numerically demonstrate a novel approach for VQAs, utilizing randomised quantum circuits to generate the variational wavefunction. We parameterize the distribution function of these random circuits using artificial neural networks and optimize it to find the solution. This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost, in terms of the sampling cost of quantum circuits. Given a fixed gate number, we can systematically increase the expressive power by extending the quantum-computing time. With a sufficiently large permissible time cost, the variational wavefunction can approximate any quantum state with arbitrary accuracy. Furthermore, we establish explicit relationships between expressive power, time cost, and gate number for variational quantum eigensolvers. These results highlight the promising potential of the random-circuit approach in achieving a high expressive power in quantum computing.

quant-ph↗

arXiv2023

Phenomenological analysis of the quasi-two-body $B \to D (R\to) K π$ decays in PQCD Approach

The quasi-two-body $B \to D (R\to) K π$ decays are calculated in PQCD approach based on the $k_T$ factorization by introducing the wave functions of $Kπ$ pair associated with the resonances $K^*(892)$, $K_0^*(1430)$ and $K_2^*(1430)$. The results show that most branching fractions are at the order of $10^{-7}$ or even smaller. However, for $B^0\to D^0(K^*\to)Kπ$ decays enhanced by the CKM element $V_{cs}$, their branching fractions are at the order of $10^{-6}$, which are measurable in the current ongoing experiments. Based on the narrow-width-approximation we also extract the branching fractions of the corresponding two-body $B \to D K^*$ decays and the results are in good agreement with previous predictions. Because these decays are only governed by the tree operators, there are no $CP$ asymmetries in these decays in the standard model.

hep-ph↗

arXiv2023

Site-selective doublon-holon dynamics in a pumped one-dimensional Hubbard superlattice with staggered Coulomb interactions

Doublon-holon dynamics is investigated in a pumped one-dimensional Hubbard model with a staggered on?site Coulomb interaction at half-filling. When the system parameters are set to be in the Mott insulating regime the equilibrium sublattice density of states exhibits several characteristic peaks, corresponding to the lower and upper Hubbard bands as well as hybridization bands. We study the linear absorption spectrum and find two main peaks characterizing the photon frequencies which excite the ground state to an excited state. For a system driven by a laser pulse with general intensity and frequency, both the energy absorption and the doublon-holon dynamics exhibit distinct behaviors as a function of laser amplitude and frequency. Single-photon processes are observed at low laser intensity where the energy is absorbed for resonance laser frequencies. For strong laser intensity multi-photon induced dynamics are observed in the system, which are confirmed by an evaluation of the Loschmidt amplitude. The contribution of multi-photon processes to site-specific double occupancy is also characterized by the generalized Loschmidt amplitude. The site-selective doublon-holon dynamics are observed in both the one and multi-photon processes and the site-selective behavior is explained within a quasiparticle picture. Our study suggests strategies to optically engineer the doublon-holon dynamics in one dimensional strongly correlated many-body systems.

cond-mat.str-el↗

arXiv2023

When Mining Electric Locomotives Meet Reinforcement Learning

As the most important auxiliary transportation equipment in coal mines, mining electric locomotives are mostly operated manually at present. However, due to the complex and ever-changing coal mine environment, electric locomotive safety accidents occur frequently these years. A mining electric locomotive control method that can adapt to different complex mining environments is needed. Reinforcement Learning (RL) is concerned with how artificial agents ought to take actions in an environment so as to maximize reward, which can help achieve automatic control of mining electric locomotive. In this paper, we present how to apply RL to the autonomous control of mining electric locomotives. To achieve more precise control, we further propose an improved epsilon-greedy (IEG) algorithm which can better balance the exploration and exploitation. To verify the effectiveness of this method, a co-simulation platform for autonomous control of mining electric locomotives is built which can complete closed-loop simulation of the vehicles. The simulation results show that this method ensures the locomotives following the front vehicle safely and responding promptly in the event of sudden obstacles on the road when the vehicle in complex and uncertain coal mine environments.

eess.SY↗

arXiv2023

Memory AMP for Generalized MIMO: Coding Principle and Information-Theoretic Optimality

To support complex communication scenarios in next-generation wireless communications, this paper focuses on a generalized MIMO (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input distributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and correlated channel matrices). The orthogonal/vector approximate message passing (OAMP/VAMP) receiver has been proved to be information-theoretically optimal in GMIMO, but it is limited to high-complexity LMMSE. To solve this problem, a low-complexity memory approximate message passing (MAMP) receiver has recently been shown to be Bayes optimal but limited to uncoded systems. Therefore, how to design a low-complexity and information-theoretically optimal receiver for GMIMO is still an open issue. To address this issue, this paper proposes an information-theoretically optimal MAMP receiver and investigates its achievable rate analysis and optimal coding principle. Specifically, due to the long-memory linear detection, state evolution (SE) for MAMP is intricately multidimensional and cannot be used directly to analyze its achievable rate. To avoid this difficulty, a simplified single-input single-output variational SE (VSE) for MAMP is developed by leveraging the SE fixed-point consistent property of MAMP and OAMP/VAMP. The achievable rate of MAMP is calculated using the VSE, and the optimal coding principle is established to maximize the achievable rate. On this basis, the information-theoretic optimality of MAMP is proved rigorously. Numerical results show that the finite-length performances of MAMP with practical optimized LDPC codes are 0.5-2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performances as OAMP/VAMP with 0.4% of the time consumption for large-scale systems.

cs.IT↗

arXiv2023

Entropy solutions to the fully non-local diffusion equations

We consider the fully non-local diffusion equations with non-negative $L^1$-data. Based on the approximation and energy methods, we prove the existence and uniqueness of non-negative entropy solutions for such problems. In particular, our results are valid for the time-space fractional Laplacian equations.

math.AP↗

arXiv2023

Low-Complexity and Information-Theoretic Optimal Memory AMP for Coded Generalized MIMO

This paper considers a generalized multiple-input multiple-output (GMIMO) with practical assumptions, such as massive antennas, practical channel coding, arbitrary input distributions, and general right-unitarily-invariant channel matrices (covering Rayleigh fading, certain ill-conditioned and correlated channel matrices). Orthogonal/vector approximate message passing (OAMP/VAMP) has been proved to be information-theoretically optimal in GMIMO, but it is limited to high complexity. Meanwhile, low-complexity memory approximate message passing (MAMP) was shown to be Bayes optimal in GMIMO, but channel coding was ignored. Therefore, how to design a low-complexity and information-theoretic optimal receiver for GMIMO is still an open issue. In this paper, we propose an information-theoretic optimal MAMP receiver for coded GMIMO, whose achievable rate analysis and optimal coding principle are provided to demonstrate its information-theoretic optimality. Specifically, state evolution (SE) for MAMP is intricately multi-dimensional because of the nature of local memory detection. To this end, a fixed-point consistency lemma is proposed to derive the simplified variational SE (VSE) for MAMP, based on which the achievable rate of MAMP is calculated, and the optimal coding principle is derived to maximize the achievable rate. Subsequently, we prove the information-theoretic optimality of MAMP. Numerical results show that the finite-length performances of MAMP with optimized LDPC codes are about 1.0 - 2.7 dB away from the associated constrained capacities. It is worth noting that MAMP can achieve the same performance as OAMP/VAMP with 0.4% of the time consumption for large-scale systems.

cs.IT↗

arXiv2023

Sub-5 nm Gate-All-Around InP Nanowire Transistors Towards High-Performance Devices

Gate-all-around (GAA) nanowire (NW) field-effect transistor (FET) is a promising device architecture due to its superior gate controllability than that of the conventional FinFET architecture. The significantly higher electron mobility of indium phosphide (InP) NW than silicon NW makes it particularly well-suited for high-performance (HP) electronics applications. In this work, we perform an ab initio quantum transport simulation to investigate the performance limit of sub-5-nm gate length (Lg) GAA InP NW FETs. The GAA InP NW FETs with Lg of 4 nm can meet the International Technology Roadmap for Semiconductors (ITRS) requirements for HP devices from the perspective of on-state current, delay time, and power dissipation. We also investigate the impact of strain on 3-nm-Lg GAA InP NW FETs. The application of tensile strain results in a remarkable increase of over 60% in the on-state current. These results highlight the potential of GAA InP NW FETs for HP applications in the sub-5-nm Lg region.

cond-mat.mtrl-sci↗

arXiv2023

PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations

In this paper, an innovative Physical Model-driven Neural Network (PMNN) method is proposed to solve time-fractional differential equations. It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives. Specifically, once the fractional differential operator is discretized, DNNs are employed as a bridge to integrate interpolation approximation techniques with differential equations. On the basis of this integration, we construct a neural-based iteration scheme. Subsequently, by training DNNs to learn this temporal iteration scheme, approximate solutions to the differential equations can be obtained. The proposed method aims to preserve the intrinsic physical information within the equations as far as possible. It fully utilizes the powerful fitting capability of neural networks while maintaining the efficiency of the difference schemes for fractional differential equations. Moreover, we validate the efficiency and accuracy of PMNN through several numerical experiments.

cs.LG↗

arXiv2023

HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional Partial Differential Equations

Neural network solvers represent an innovative and promising approach for tackling time-fractional partial differential equations by utilizing deep learning techniques. L1 interpolation approximation serves as the standard method for addressing time-fractional derivatives within neural network solvers. However, we have discovered that neural network solvers based on L1 interpolation approximation are unable to fully exploit the benefits of neural networks, and the accuracy of these models is constrained to interpolation errors. In this paper, we present the high-precision Hermite Neural Solver (HNS) for solving time-fractional partial differential equations. Specifically, we first construct a high-order explicit approximation scheme for fractional derivatives using Hermite interpolation techniques, and rigorously analyze its approximation accuracy. Afterward, taking into account the infinitely differentiable properties of deep neural networks, we integrate the high-order Hermite interpolation explicit approximation scheme with deep neural networks to propose the HNS. The experimental results show that HNS achieves higher accuracy than methods based on the L1 scheme for both forward and inverse problems, as well as in high-dimensional scenarios. This indicates that HNS has significantly improved accuracy and flexibility compared to existing L1-based methods, and has overcome the limitations of explicit finite difference approximation methods that are often constrained to function value interpolation. As a result, the HNS is not a simple combination of numerical computing methods and neural networks, but rather achieves a complementary and mutually reinforcing advantages of both approaches. The data and code can be found at \url{https://github.com/hsbhc/HNS}.

cs.LG↗

arXiv2023

Observing parity-time symmetry in diffusive systems

Phase modulation has scarcely been mentioned in diffusive systems since the diffusion process does not carry momentum like waves. Recently, the non-Hermitian physics provides a new perspective for understanding diffusion and shows prospects in the phase regulation of heat flow, for example, the discovery of anti-parity-time (APT) symmetry in diffusive systems. The precise control of thermal phase however remains elusive hitherto and can hardly be realized in APT-symmetric thermal systems due to the existence of phase oscillation. Here we construct the counterpart of APT-symmetric diffusive systems, i.e., PT-symmetric diffusive systems, which can achieve complete suppression of thermal phase oscillation. We find the real coupling of diffusive fields can be readily established through a strong convective background, where the decay-rate detuning is enabled by thermal metamaterial design. Moreover, we observe phase transition of PT symmetry breaking in diffusive systems with the symmetry-determined amplitude distribution and phase regulation of coupled temperature fields. Our work uncovers the existence of PT-symmetry in dissipative energy exchanges and provides a unique approach for harnessing the mass transfer of particles, the wave propagation in strongly scattering systems as well as thermal conduction.

physics.app-ph↗

arXiv2023

The Properties of Fractional Gaussian Process and Their Applications

The process $(G_t)_{t\in[0,T]}$ is referred to as a fractional Gaussian process if the first-order partial derivative of the difference between its covariance function and that of the fractional Brownian motion $(B^H_t)_{t\in[0,T ]}$ is a normalized bounded variation function. We quantify the relation between the associated reproducing kernel Hilbert space of $(G)$ and that of $(B^H)$. Seven types of Gaussian processes with non-stationary increments in the literature belong to it. In the context of applications, we demonstrate that the Gladyshev's theorem holds for this process, and we provide Berry-Esséen upper bounds associated with the statistical estimations of the ergodic fractional Ornstein-Uhlenbeck process driven by it. The second application partially builds upon the idea introduced in \cite{BBES 23}, where they assume that $(G)$ has stationary increments. Additionally, we briefly discuss a variant of this process where the covariance structure is not entirely linked to that of the fractional Brownian motion.

math.PR↗

arXiv2023

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants are required to recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides a large amount of unlabeled samples for semi-supervised learning. In this paper, we introduce the motivation behind this challenge, describe the benchmark dataset, and provide some statistics about participants. To continue using this dataset after MER 2023, please sign a new End User License Agreement and send it to our official email address merchallenge.contact@gmail.com. We believe this high-quality dataset can become a new benchmark in multimodal emotion recognition, especially for the Chinese research community.

cs.CL↗

arXiv2023

Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems with Limited Feedback

The downlink channel covariance matrix (CCM) acquisition is the key step for the practical performance of massive multiple-input and multiple-output (MIMO) systems, including beamforming, channel tracking, and user scheduling. However, this task is challenging in the popular frequency division duplex massive MIMO systems with Type I codebook due to the limited channel information feedback. In this paper, we propose a novel formulation that leverages the structure of the codebook and feedback values for an accurate estimation of the downlink CCM. Then, we design a cutting plane algorithm to consecutively shrink the feasible set containing the downlink CCM, enabled by the careful design of pilot weighting matrices. Theoretical analysis shows that as the number of communication rounds increases, the proposed cutting plane algorithm can recover the ground-truth CCM. Numerical results are presented to demonstrate the superior performance of the proposed algorithm over the existing benchmark in CCM reconstruction.

eess.SP↗

arXiv2023

Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm.

cs.CV↗

arXiv2023

Quantum algorithms for optimal effective theory of many-body systems

A common situation in quantum many-body physics is that the underlying theories are known but too complicated to solve efficiently. In such cases one usually builds simpler effective theories as low-energy or large-scale alternatives to the original theories. Here the central tasks are finding the optimal effective theories and proving their equivalence to the original theories. Recently quantum computing has shown the potential of solving quantum many-body systems by exploiting its inherent parallelism. It is thus an interesting topic to discuss the emergence of effective theories and design efficient tools for finding them based on the results from quantum computing. As the first step towards this direction, in this paper, we propose two approaches that apply quantum computing to find the optimal effective theory of a quantum many-body system given its full Hamiltonian. The first algorithm searches the space of effective Hamiltonians by quantum phase estimation and amplitude amplification. The second algorithm is based on a variational approach that is promising for near-future applications.

quant-ph↗

arXiv2023

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models

The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proliferation, thus posing challenges for modeling dynamical systems with high-dimensional latent states. To surmount this obstacle, we propose to integrate the efficient transformed Gaussian process (ETGP) into the GPSSM, which involves pushing a shared GP through multiple normalizing flows to efficiently model the transition function in high-dimensional latent state space. Additionally, we develop a corresponding variational inference algorithm that surpasses existing methods in terms of parameter count and computational complexity. Experimental results on diverse synthetic and real-world datasets corroborate the efficiency of the proposed method, while also demonstrating its ability to achieve similar inference performance compared to existing methods. Code is available at \url{https://github.com/zhidilin/gpssmProj}.

cs.LG↗

arXiv2023

Global Existence of Large Solutions for the 3D incompressible Navier--Stokes--Poisson--Nernst--Planck Equations

This work is concerned with the global existence of large solutions to the three-dimensional dissipative fluid-dynamical model, which is a strongly coupled nonlinear nonlocal system characterized by the incompressible Navier--Stokes--Poisson--Nernst--Planck equations. Making full use of the algebraic structure of the system, we obtain the global existence of solutions without smallness assumptions imposed on the third component of the initial velocity field and the summation of initial densities of charged species. More precisely, we prove that there exist two positive constants $c_{0}, C_{0}$ such that if the initial data satisfies \begin{align*} \big(\|u_{0}^{h}\|_{\dot{B}^{-1+\frac{3}{p}}_{p,1}}+\|N_{0}-P_{0}\|_{\dot{B}^{-2+\frac{3}{q}}_{q,1}} \big) \exp\Big\{C_{0}\big(\|u_{0}^{3}\|_{\dot{B}^{-1+\frac{3}{p}}_{p,1}}^{2}+(\|N_{0}+P_{0}\|_{\dot{B}^{-2+\frac{3}{r}}_{r,1}}+1)\exp\big\{C_{0}\|u_{0}^{3}\|_{\dot{B}^{-1+\frac{3}{p}}_{p,1}}\big\}+1\big)\Big\} \leq c_{0}, \end{align*} then the incompressible Navier--Stokes--Poisson--Nernst--Planck equations admits a unique global solution.

math.AP↗