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Yongqiang Li

Publications and source records attributed to Yongqiang Li.

At least 19 recordsLinked to original sources

Nagaoka supermetal in the particle-doped triangular Hubbard model

While the interplay of correlations and geometric frustration in doped Mott insulators provides a fertile ground for exotic quantum phases, the nature of the metallic state emerging upon particle doping remains poorly understood. In this work, we investigate the triangular-lattice Hubbard model with particle doping and provide compelling evidence for an intrinsic, interaction-driven quantum state, which we term the Nagaoka supermetal. This state is characterized by a sublinear temperature dependence in the DC resistivity, along with singular behaviors in the charge compressibility and zero-frequency spectral weight. To understand the origin of these singular properties, we derive an effective low-energy model and demonstrate that a higher-order Van Hove singularity emerges from the reconstructed dispersion. This singularity gives rise to a power-law divergence in the density of states, capturing the anomalous properties observed in the supermetallic regime. Our findings offer a new perspective on non-Fermi liquid states in geometrically frustrated systems and are directly accessible in current ultracold atom experiments.

cond-mat.quant-gas

Statistics-governed dynamical scaling in interacting anyonic chains

Particle statistics impose fundamental constraints on nonequilibrium quantum dynamics, yet it remains an open question whether anyonic statistics can lead to emergent dynamical scaling beyond the conventional Bose-Fermi paradigm. Here we investigate the far-from-equilibrium many-body relaxation of anyons in a one-dimensional lattice, uncovering a statistics-governed, robust scaling behavior that deviates from standard Bose-Fermi limits. Based on large-scale numerical simulations and scaling analysis, we find that in the weakly interacting regime, anyonic statistics leads to emergent superdiffusive scaling in particle transport, while the entanglement entropy remains ballistic and is essentially insensitive to exchange statistics. The anomalous dynamics can be interpreted intuitively from the statistical-phase-induced quantum interference that suppresses coherent holon-doublon propagation; in contrast, the entanglement growth is dominated by its configurational component, which maintains ballistic spreading regardless of the statistical phase. Our results establish anyonic statistics as a distinct source of universal nonequilibrium dynamics beyond bosons and fermions, with direct relevance to current quantum simulation experiments.

cond-mat.quant-gas

Reinforcement Learning-Based Secure Near-field Directional Modulation Enhanced by Rotatable RIS

This paper investigates secure Directional Modulation (DM) design enhanced by a rotatable active Reconfigurable Intelligent Surface (RIS). In conventional RIS-assisted DM networks, the security performance gain is limited due to the multiplicative path loss introduced by the RIS reflection path. To address this challenge, a Secrecy Rate (SR) maximization problem is formulated, subject to constraints including the eavesdropper's Direction Of Arrival (DOA) estimation performance, transmit power, rotatable range, and maximum reflection amplitude of the RIS elements. To solve this non-convex optimization problem, three algorithms are proposed: a multi-stream null-space projection and leakage-based method, an enhanced leakage-based method, and an optimization scheme based on the Distributed Soft Actor-Critic with Three refinements (DSAC-T). Simulation results validate the effectiveness of the proposed algorithms. A performance trade-off is observed between eavesdropper's DOA estimation accuracy and the achievable SR. The security enhancement provided by the RIS is more significant in systems equipped with a small number of antennas. By optimizing the orientation of the RIS, a 52.6\% improvement in SR performance can be achieved.

eess.SP

Which Channel in 6G, Low-rank or Full-rank, more needs RIS from a Perspective of DoF?

Reconfigurable intelligent surface (RIS), as an efficient tool to improve receive signal-to-noise ratio, extend coverage and create more spatial diversity, is viewed as a most promising technique for the future wireless networks like 6G. As you know, RIS is very suitable for a special wireless scenario with wireless link between BS and users being completely blocked, i.e., no link. In this paper, we extend its applications to a general scenario, i.e., rank-deficient channel, particularly some extremely low-rank ones such as no link, and line-of-sight (LoS, rank-one). Actually, there are several potential important low-rank applications like low-altitude, satellite, UAV, marine, and deep-space communications. In such a situation, it is found that RIS may make a dramatic degrees of freedom (DoF) enhancement over no RIS. By using a distributed RISs placement, the DoF of channel from BS to user in LoS channel may be even boosted from a low-rank like 0/1 to full-rank. This will achieve an extremely rate improvement via spatial parallel multiple-stream transmission from BS to user. In this paper, we present a complete review of making an in-depth discussion on DoF effect of RIS.

eess.SP

Homomorphic Mappings for Value-Preserving State Aggregation in Markov Decision Processes

State aggregation aims to reduce the computational complexity of solving Markov Decision Processes (MDPs) while preserving the performance of the original system. A fundamental challenge lies in optimizing policies within the aggregated, or abstract, space such that the performance remains optimal in the ground MDP-a property referred to as {"}optimal policy equivalence {"}. This paper presents an abstraction framework based on the notion of homomorphism, in which two Markov chains are deemed homomorphic if their value functions exhibit a linear relationship. Within this theoretical framework, we establish a sufficient condition for the equivalence of optimal policy. We further examine scenarios where the sufficient condition is not met and derive an upper bound on the approximation error and a performance lower bound for the objective function under the ground MDP. We propose Homomorphic Policy Gradient (HPG), which guarantees optimal policy equivalence under sufficient conditions, and its extension, Error-Bounded HPG (EBHPG), which balances computational efficiency and the performance loss induced by aggregation. In the experiments, we validated the theoretical results and conducted comparative evaluations against seven algorithms.

cs.LG

Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrations

An intelligent driving system should dynamically formulate appropriate driving strategies based on the current environment and vehicle status while ensuring system security and reliability. However, methods based on reinforcement learning and imitation learning often suffer from high sample complexity, poor generalization, and low safety. To address these challenges, this paper introduces an efficient and generalized end-to-end autonomous driving system (EGADS) for complex and varied scenarios. The RL agent in our EGADS combines variational inference with normalizing flows, which are independent of distribution assumptions. This combination allows the agent to capture historical information relevant to driving in latent space effectively, thereby significantly reducing sample complexity. Additionally, we enhance safety by formulating robust safety constraints and improve generalization and performance by integrating RL with expert demonstrations. Experimental results demonstrate that, compared to existing methods, EGADS significantly reduces sample complexity, greatly improves safety performance, and exhibits strong generalization capabilities in complex urban scenarios. Particularly, we contributed an expert dataset collected through human expert steering wheel control, specifically using the G29 steering wheel.

cs.RO

Universal scaling of higher-order cumulants in quantum isotropic spin chains

Understanding universal behavior of far-from-equilibrium transport dynamics at a quantum many body level is a longstanding challenge. In particular, a full characterization of universal dynamics of nonlocal correlation functions still remains largely unknown. In this letter, we uncover universal scaling laws of higher-order cumulants in one-dimensional isotropic Heisenberg model, revealing anomalous behaviors of nonequilibrium dynamics exclusively accessible in higher-order correlations. By means of numerical simulations and full counting statistics, we determine the power laws of both the spin polarization transfer and contrast cumulants for different kinds of helix and domain-wall initial states. Building on such physical states, we unify the scaling behavior of the higher-order cumulants, giving rise to two types of dynamics: anomalous diffusive and superdiffusive. For the former, these higher cumulants show a deviation from Gaussian statistics, with the scaling exponents being identical for the first four orders. For the latter, however, we observe a breakdown of KPZ universality, with the exponents of the third and fourth orders differing significantly from those of the first two. Our results are also agreeable with recent experimental observations, advancing understanding of far-from-equilibrium transport phenomena.

cond-mat.quant-gas

$p$-orbital self-organization of ultracold atoms coupled to optical cavities

Atoms coupled to optical cavities provide a novel platform for understanding high-orbital exotic phenomena in strongly correlated materials. In this study, we investigate strongly correlated ultracold bosonic gases that are coupled to two orthogonally arranged optical cavities and driven by a blue-detuned running-wave laser field. Our results demonstrate that atoms initially in the $s$-orbital state can be scattered into $p_x$- and $p_y$-orbital states in either a symmetric or asymmetric manner, depending on the frequencies of the two cavities. For the symmetric configuration, we observe that atoms are scattered into the $p_x$- and $p_y$-orbitals equally. In the asymmetric case, photons emitted into one cavity mode suppress the scattering into the orthogonal mode. Notably, the coupling of atoms with multiple cavity modes leads to the emergence of high-orbital self-organized phases, accompanied by orbital-density waves that break different symmetries.

cond-mat.quant-gas

Nonequilibrium magnetic dynamics of the two-component Bose-Hubbard model

A central challenge in strongly interacting many-body systems is understanding the far-from-equilibrium dynamics. Here, we study the many-body magnetic dynamics of the two-component Bose-Hubbard model by developing a two-component extension of nonequilibrium bosonic dynamical mean-field theory. Using this numerical method, we uncover rich quantum spin dynamics via inter-species interaction quenches. A sudden ramp-up of interactions induces slow thermalization, leading to a long-lived metastable state, whereas quenching to weak interactions results in rapid thermal equilibrium, featuring a two-step relaxation behavior through distinct exponential decays. Furthermore, under periodic modulation of the inter-species interactions, emergent Floquet dynamics drives a transition from a magnetic to an unordered phase.

cond-mat.quant-gas

Topology-ferrimagnetism intertwining via weak interactions in Lieb lattices

A common wisdom about quantum many-body systems is that emergent phases typically fall into either the Landau-Ginzburg paradigm or topological classifications. Experimentally realizing the intertwined emergence of spontaneous symmetry breaking and topological order remains challenging. Here, we present an experimentally accessible platform for studying magnetic topological states in a spin-orbit-coupled Lieb lattice. Remarkably, we observe the coexistence of topological characteristics, quantified by the Chern number and Bott index, with spontaneous symmetry-breaking orders, such as ferrimagnetism, in the many-body ground states. Computational analyses combining dynamical mean-field theory and Hartree-Fock approximations reveal a pronounced parameter regime where magnetic topological insulators emerge even under weak interactions. This unconventional phenomenon originates from the Lieb lattice's unique band structure, which facilitates the synergy between interaction-driven symmetry breaking and spin-orbit coupling induced band inversion. Crucially, spin polarization and spin winding co-emerge as inherently coupled phenomena due to their shared origin in the same interacting, spinful atoms. We further propose a specific experimental implementation scheme for ultracold atoms, utilizing currently available Raman lattice techniques. Our findings pave the way for exploring the interplay between symmetry-broken states and topological order in strongly correlated systems.

cond-mat.quant-gas

Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network

In this paper, the channel estimation (CE) of intelligent reflecting surface-aided near-field (NF) multi-user communication is investigated. Initially, the least square (LS) estimator and minimum mean square error (MMSE) estimator for the estimated channel are designed, their mean square errors (MSEs) are derived, and the Cramer-Rao lower bound (CRLB) is derived to serve as a benchmark for performance evaluation. Subsequently, in view of the fact that the NF channel model is more sensitive to distance variations compared to the far-field model, this leads to pronounced discrepancies in the user channel characteristics in different regions. To effectively capture and utilize these diverse channel features, users are initially divided into distinct regions predicated on pivotal parameters, such as channel angle and distance. Correspondingly, a user region classifier based on convolutional neural networks is designed. Then, to fully harness the potential of deep residual networks (DRNs) in denoising, the aforementioned CE problem is reconceptualized as a denoising task, and a DRN-driven single region NF CE network, named SR-DRN-NFCE, is proposed. In addition, by integrating SR-DRN-NFCE networks corresponding to different regions and conducting joint training in a federated learning (FL) manner, a new network is obtained, named FL-DRN-NFCE. Simulation results demonstrate that the proposed FL-DRN-NFCE network outperforms LS, MMSE, and no residual connections in terms of MSE, and the proposed FL-DRN-NFCE method has higher CE accuracy than the SR-DRN-NFCE method.

eess.SP

Stable and High-Precision 3D Positioning via Tunable Composite-Dimensional Hong-Ou-Mandel Interference

We propose a stable and high-precision three-dimensional (3D) quantum positioning scheme based on Hong-Ou-Mandel interference. While previous studies have explored HOM interference in quantum metrology, they were mostly limited to one-dimensional scenarios, whereas real-world applications require full 3D spatial resolution. Our approach not only generalizes HOM positioning to 3D-achieving ultimate sensitivity as defined by the quantum Cramer-Rao bound-but also stabilizes estimation accuracy through simple polarization tuning, ensuring that the Fisher information remains independent of the estimated parameters. Theoretical analysis and simulations demonstrate that our method achieves ultra-precise and reliable 3D positioning, even with a limited number of detected photons.

quant-ph

Emergence of topological states in relaxation dynamics of interacting bosons

Topological concepts have been employed to understand the ground states of many strongly correlated systems, but it is still quite unclear if and how topology manifests itself in the relaxation dynamics. Here we uncover emergent topological phenomena in the time evolution of far-from-equilibrium one-dimensional interacting bosons. Beginning with simple product states, the system evolves into long-time stationary states with high energy that are nonthermal for a wide range of parameters, and they exhibit nonlocal string correlation that is characteristic of the symmetry-protected topological ground state of the Hamiltonian. In contrast, no topological feature is found in the stationary state as long as the system thermalizes. This difference is further corroborated by the distinct behaviour of quantum entanglement and edge states of the system. Our theoretical prediction can be examined by current experimental techniques and paves the way for a more comprehensive understanding of topological phases in nonequilibrium settings.

cond-mat.quant-gas

Multi-Modality Driven LoRA for Adverse Condition Depth Estimation

The autonomous driving community is increasingly focused on addressing corner case problems, particularly those related to ensuring driving safety under adverse conditions (e.g., nighttime, fog, rain). To this end, the task of Adverse Condition Depth Estimation (ACDE) has gained significant attention. Previous approaches in ACDE have primarily relied on generative models, which necessitate additional target images to convert the sunny condition into adverse weather, or learnable parameters for feature augmentation to adapt domain gaps, resulting in increased model complexity and tuning efforts. Furthermore, unlike CLIP-based methods where textual and visual features have been pre-aligned, depth estimation models lack sufficient alignment between multimodal features, hindering coherent understanding under adverse conditions. To address these limitations, we propose Multi-Modality Driven LoRA (MMD-LoRA), which leverages low-rank adaptation matrices for efficient fine-tuning from source-domain to target-domain. It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning(VTCCL). During PDDA, the image encoder with MMD-LoRA generates target-domain visual representations, supervised by alignment loss that the source-target difference between language and image should be equal. Meanwhile, VTCCL bridges the gap between textual features from CLIP and visual features from diffusion model, pushing apart different weather representations (vision and text) and bringing together similar ones. Through extensive experiments, the proposed method achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets, underscoring robustness and efficiency in adapting to varied adverse environments.

cs.CV

WPDA: Frequency-based Backdoor Attack with Wavelet Packet Decomposition

This work explores an emerging security threat against deep neural networks (DNNs) based image classification, i.e., backdoor attack. In this scenario, the attacker aims to inject a backdoor into the model by manipulating training data, such that the backdoor could be activated by a particular trigger and bootstraps the model to make a target prediction at inference. Currently, most existing data poisoning-based attacks struggle to achieve success at low poisoning ratios, increasing the risk of being defended by defense methods. In this paper, we propose a novel frequency-based backdoor attack via Wavelet Packet Decomposition (WPD), WPD decomposes the original image signal to a spectrogram that contains frequency information with different semantic meanings. We leverage WPD to statistically analyze the frequency distribution of the dataset to infer the key frequency regions the DNNs would focus on, and the trigger information is only injected into the key frequency regions. Our method mainly includes three parts: 1) the selection of the poisoning frequency regions in spectrogram; 2) trigger generation; 3) the generation of the poisoned dataset. Our method is stealthy and precise, evidenced by the 98.12% Attack Success Rate (ASR) on CIFAR-10 with the extremely low poisoning ratio 0.004% (i.e., only 2 poisoned samples among 50,000 training samples) and can bypass most existing defense methods. Besides, we also provide visualization analyses to explain why our method works.

cs.CR

Orbital phases of $p$-band ultracold fermions in the frustrated triangular lattice

Orbital degrees of freedom play an important role for understanding the emergence of unconventional quantum phases. Ultracold atomic gases in optical lattices provide a wonderful platform to simulate orbital physics. In this work, we consider spinless fermionic atoms loaded into $p$-orbital bands of a two-dimensional frustrated triangular lattice. The system can be described by an extended Fermi-Hubbard model, which is numerically solved by using the orbital version of real-space dynamical mean-field theory. Low-temperature phase diagrams are obtained, which contain stripe-, ferro- and para-orbital ordered quantum phases, due to the interplay of anisotropic hoppings and geometrical frustration. In order to understand the underlying mechanics of competing orbital orders, we derive an effective orbital-exchange model, which yields consistent explanation with our main numerical results.

cond-mat.quant-gas

Orion-14B: Open-source Multilingual Large Language Models

In this study, we introduce Orion-14B, a collection of multilingual large language models with 14 billion parameters. We utilize a data scheduling approach to train a foundational model on a diverse corpus of 2.5 trillion tokens, sourced from texts in English, Chinese, Japanese, Korean, and other languages. Additionally, we fine-tuned a series of models tailored for conversational applications and other specific use cases. Our evaluation results demonstrate that Orion-14B achieves state-of-the-art performance across a broad spectrum of tasks. We make the Orion-14B model family and its associated code publicly accessible https://github.com/OrionStarAI/Orion, aiming to inspire future research and practical applications in the field.

cs.CL

Entanglement-Enhanced Quantum Strategies for Accurate Estimation of Multibody Group Motion and Moving Object Characteristics

This study presents a quantum strategy for simultaneous estimation of two physical quantities using different entanglement resources. We explore the utilization of positively or negatively timecorrelated photons. The proposed method enables the detection of central position and relative velocity of multibody systems, as well as precise measurement of size and velocity of moving objects. Comparative analysis with other strategies reveals the superior quantum advantage of our approach, particularly when appropriate entanglement sources with high entanglement degree are employed. These findings contribute to advancing our understanding of quantum strategies for accurate measurements.

quant-ph