arXiv subjects

Yiding Wang

Publications and source records attributed to Yiding Wang.

33 records · Page 2Linked to original sources

arXiv2025

Notes on detection and measurement of quantum coherence

Quantum coherence is one of the most basic characteristics of quantum mechanics. Here we give some methods to detect and measure quantum coherence. Firstly, we propose a coherence criterion without full quantum state tomography based on partial transposition. Moreover, we present a coherent nonlinear detection strategy from witnesses, in which we find that for some coherent states, normal witness detection fails but our nonlinear detection succeeds. In addition, we prove that when the nonlinear detection on the two copies of the coherent state fails, the nonlinear detection on the three copies may be successful. Finally, due to the difficulty in calculating robustness of coherence for general states, we introduce a lower bound for coherent robustness based on the witness operator, and after comparing our lower bound with the currently known lower bound, one show that our lower bound is better. Coherence is believed to play a crucial role in quantum information tasks, making the detection and quantization of coherence particularly significant. Therefore, these results help to open up new avenues for advancement in quantum theory.

quant-ph↗

arXiv2025

Type III Valley Polarization and Anomalous Valley Hall Effect in Two-Dimensional Non-Janus and Janus Altermagnet Fe2WS2Se2

Exploiting the valley degree of freedom introduces a novel paradigm for advancing quantum information technology. Currently, the investigation on spontaneous valley polarization mainly focuses on two major types of systems. One type magnetic systems by breaking the time-reversal symmetry, the other is ferroelectric materials through breaking the inversion symmetry. Might there be additional scenarios? Here, we propose to realize spontaneous valley polarization by breaking the mirror symmetry in the altermagnets, named type III valley polarization. Through symmetry analysis and first-principles calculations, we confirm that this mechanism is feasible in Non-Janus Fe2WS2Se2. Monolayer Non-Janus and Janus Fe2WS2Se2 are stable Neel-type antiferromagnetic state with the direct band gap semiconductor. More interestingly, their magnetic anisotropy energy exhibits the rare biaxial anisotropy and a four-leaf clover shape in the xy plane, while the xz and yz planes show the common uniaxial anisotropy. This originated from the fourth-order single ion interactions. More importantly, the valley splitting is spontaneously generated in the Non-Janus Fe2WS2Se2 due to the Mxy symmetry breaking, without requiring the SOC effect. Both the Non-Janus and Janus Fe2WS2Se2 exhibit diverse valley polarization and anomalous valley Hall effect properties. In addition, the magnitude and direction of valley polarization can be effectively tuned by the biaxial strain and magnetic field. Our findings not only expand the realization system of spontaneous valley polarization, but also provide a theoretical basis for the high-density storage of valley degrees of freedom.

cond-mat.mtrl-sci↗

arXiv2025

Quantifying quantum-state texture

Quantum-state texture is a newly recognized quantum resource that has garnered attention with the advancement of quantum theory. In this work, we introduce several potential quantum-state texture measure schemes and check whether they satisfy the three fundamental conditions required for a valid quantum-state texture measure. Specifically, the measure induced by the l_1-norm serves as a vital tool for quantifying coherence, but we prove that it cannot be used to quantify quantum state texture. Furthermore, we show that while relative entropy and robustness meet three fundamental conditions, they are not optimal for quantifying quantum-state texture. Fortunately, we still find that there are several measures that can be used as the measure standard of quantum-state texture. Among them, the trace distance measure and the geometric measure are two good measurement schemes. In addition, the two measures based on Uhlmann's fidelity are experimentally friendly and can serve as an ideal definition of quantum-state texture measures in non-equilibrium situations. All these researches on quantum-state texture measure theory can enrich the resource theory framework of quantum-state texture.

quant-ph↗

arXiv2025

Baichuan 2: Open Large-scale Language Models

Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering. However, most powerful LLMs are closed-source or limited in their capability for languages other than English. In this technical report, we present Baichuan 2, a series of large-scale multilingual language models containing 7 billion and 13 billion parameters, trained from scratch, on 2.6 trillion tokens. Baichuan 2 matches or outperforms other open-source models of similar size on public benchmarks like MMLU, CMMLU, GSM8K, and HumanEval. Furthermore, Baichuan 2 excels in vertical domains such as medicine and law. We will release all pre-training model checkpoints to benefit the research community in better understanding the training dynamics of Baichuan 2.

cs.CL↗

arXiv2025

Distribution Relationship of Quantum Battery Capacity

The distribution relationship of quantum battery capacity is investigated. First, it is proved that for two-qubit X-states, the sum of the subsystem battery capacities does not exceed the total system's battery capacity, and the conditions are provided under which they are equal. Then define the difference between the total system's and subsystems'battery capacities as the residual battery capacity (RBC) and show that this can be divided into coherent and incoherent components. Furthermore, it is observed that this capacity monogamy relation for quantum batteries extends to general n-qubit X states and any n-qubit X state's battery capacity distribution can be optimized to achieve capacity gain through an appropriate global unitary evolution. Specifically, for general three-qubit X states, stronger distributive relations are derived for battery capacity. Quantum batteries are believed to hold significant potential for outperforming classical counterparts in the future. These findings contribute to the development and enhancement of quantum battery theory.

quant-ph↗

arXiv2025

Witness based nonlinear detection of quantum entanglement

We present a nonlinear entanglement detection strategy which detects entanglement that the linear detection strategy fails. We show that when the nonlinear entanglement detection strategy fails to detect the entanglement of an entangled state with two copies, it may succeed with three or more copies. Based on our strategy, a witness combined with a suitable quanutm mechanical observable may detect the entanglement that can not be detected by the witness alone. Moreover, our strategy can also be applied to detect multipartite entanglement by using the witnesses for bipartite systems, as well as to entanglement concentrations.

quant-ph↗

arXiv2025

A Deep Spatio-Temporal Architecture for Dynamic Effective Connectivity Network Analysis Based on Dynamic Causal Discovery

Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain. Although the existing causal discovery methods have shown promising results in effective connectivity network analysis, they often overlook the dynamics of causality, in addition to the incorporation of spatio-temporal information in brain activity data. To address these issues, we propose a deep spatio-temporal fusion architecture, which employs a dynamic causal deep encoder to incorporate spatio-temporal information into dynamic causality modeling, and a dynamic causal deep decoder to verify the discovered causality. The effectiveness of the proposed method is first illustrated with simulated data. Then, experimental results from Philadelphia Neurodevelopmental Cohort (PNC) demonstrate the superiority of the proposed method in inferring dECNs, which reveal the dynamic evolution of directed flow between brain regions. The analysis shows the difference of dECNs between young adults and children. Specifically, the directed brain functional networks transit from fluctuating undifferentiated systems to more stable specialized networks as one grows. This observation provides further evidence on the modularization and adaptation of brain networks during development, leading to higher cognitive abilities observed in young adults.

cs.LG↗

arXiv2025

MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection

The selection of Gaussian kernel parameters plays an important role in the applications of support vector classification (SVC). A commonly used method is the k-fold cross validation with grid search (CV), which is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new approach is proposed to train SVC and optimize the selection of Gaussian kernel parameters. We first formulate the training and the parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is not needed. We then propose a projected gradient algorithm (PGA) for the training of L2-SVC-NCH. It is revealed that the famous sequential minimal optimization (SMO) algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility than the SMO. Furthermore, the solution of the maximization problem is done by a gradient ascent algorithm with dynamic learning rate. The comparative experiments between MaxMin-L2-SVC-NCH and the previous best approaches on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained while maintaining competitive test accuracy. These findings indicate that MaxMin-L2-SVC-NCH is a better choice for SVC tasks.

cs.LG↗

arXiv2024

Scaling Law for Language Models Training Considering Batch Size

Large language models (LLMs) have made remarkable advances in recent years, with scaling laws playing a critical role in this rapid progress. In this paper, we empirically investigate how a critical hyper-parameter, i.e., the global batch size, influences the LLM training prdocess. We begin by training language models ranging from 125 million to 2.6 billion parameters, using up to 300 billion high-quality tokens. Through these experiments, we establish a basic scaling law on model size and training data amount. We then examine how varying batch sizes and learning rates affect the convergence and generalization of these models. Our analysis yields batch size scaling laws under two different cases: with a fixed compute budget, and with a fixed amount of training data. Extrapolation experiments on models of increasing sizes validate our predicted laws, which provides guidance for optimizing LLM training strategies under specific resource constraints.

cs.CL↗

arXiv2024

Multifield tunable valley splitting and anomalous valley Hall effect in two-dimensional antiferromagnetic MnBr

Compared to the ferromagnetic materials that realize the anomalous valley Hall effect by breaking time-reversal symmetry and spin-orbit coupling, the antiferromagnetic materials with the joint spatial inversion and time-reversal (PT) symmetry are rarely reported that achieve the anomalous valley Hall effect. Here, we predict that the antiferromagnetic monolayer MnBr possesses spontaneous valley polarization. The valley splitting of valence band maximum is 21.55 meV at K and K' points, which is originated from Mn-dx2-y2 orbital by analyzing the effective Hamiltonian. Importantly, monolayer MnBr has zero Berry curvature in the entire momentum space but non-zero spin-layer locked Berry curvature, which offers the condition for the anomalous valley Hall effect. In addition, the magnitude of valley splitting can be signally tuned by the onsite correlation, strain, magnetization rotation, electric field, and built-in electric field. The electric field and built-in electric field induce spin splitting due to breaking the P symmetry. Therefore, the spin-layer locked anomalous valley Hall effect can be observed in MnBr. More remarkably, the ferroelectric substrate Sc2CO2 can tune monolayer MnBr to realize the transition from metal to valley polarization semiconductor. Our findings not only extend the implementation of the anomalous valley Hall effect, but also provides a platform for designing low-power and non-volatile valleytronics devices.

cond-mat.mtrl-sci↗

arXiv2024

Molecular dynamics simulations of interaction between a super edge dislocation and interstitial dislocation loops in irradiated L12-Ni3Al

The study employed MD simulations to investigate the interactions between a <110> super-edge dislocation, consisting of the four Shockley partials, and interstitial dislocation loops (IDLs) in irradiated L12-Ni3Al. Accounting for symmetry breakage in the L12 lattice, the superlattice planar faults with four distinct fault vectors have been considered for different IDL configurations. The detailed dislocation reactions and structural evolution events were identified as the four partials interacted with various IDL configurations. The slipping characteristics of Shockley partials within the IDLs and the resultant shearing and looping mechanisms were also clarified, revealing distinct energetic transition states determined by the fault vectors after the Shockley partials sweeping the IDL. Furthermore, significant variations in critical resolved shear stress (CRSS) required for the super-edge dislocation to move past the IDL were observed, attributed to various sizes and faulted vectors of enclosed superlattice planar faults in the IDLs. The current study extends the existing dislocation-IDL interaction theory from pristine FCC to L12 lattice, advances the understanding of irradiation hardening effects in L12-Ni3Al, and suggests potential applicability to other L12 systems.

cond-mat.mtrl-sci↗

arXiv2024

Moments based entanglement criteria and measures

Quantum entanglement plays a key role in quantum computation and quantum information processing. It is of great significance to find efficient and experimentally friend separability criteria to detect entanglement. In this paper, we firstly propose two easily used entanglement criteria based on matrix moments. The first entanglement criterion only uses the first two realignment moments of a density matrix. The second entanglement criterion is based on the moments related to the partially transposed matrix. By detailed examples we illustrate the effectiveness of these criteria in detecting entanglement. Moreover, we provide an experimentally measurable lower bound of concurrence based on these moments. Finally, we present both bipartite and genuine tripartite entanglement measures based on the moments of the reduced states. By detailed examples, we show that our entanglement measures characterize the quantum entanglement in a more fine ways than the existing measures.

quant-ph↗

arXiv2023

Chain of Images for Intuitively Reasoning

The human brain is naturally equipped to comprehend and interpret visual information rapidly. When confronted with complex problems or concepts, we use flowcharts, sketches, and diagrams to aid our thought process. Leveraging this inherent ability can significantly enhance logical reasoning. However, current Large Language Models (LLMs) do not utilize such visual intuition to help their thinking. Even the most advanced version language models (e.g., GPT-4V and LLaVA) merely align images into textual space, which means their reasoning processes remain purely verbal. To mitigate such limitations, we present a Chain of Images (CoI) approach, which can convert complex language reasoning problems to simple pattern recognition by generating a series of images as intermediate representations. Furthermore, we have developed a CoI evaluation dataset encompassing 15 distinct domains where images can intuitively aid problem-solving. Based on this dataset, we aim to construct a benchmark to assess the capability of future multimodal large-scale models to leverage images for reasoning. In supporting our CoI reasoning, we introduce a symbolic multimodal large language model (SyMLLM) that generates images strictly based on language instructions and accepts both text and image as input. Experiments on Geometry, Chess and Common Sense tasks sourced from the CoI evaluation dataset show that CoI improves performance significantly over the pure-language Chain of Thoughts (CoT) baselines. The code is available at https://github.com/GraphPKU/CoI.

cs.CV↗

arXiv2023

Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing

Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes one step further by skipping computing and communication through DNN layer freezing. Our key insight is that the training progress of internal DNN layers differs significantly, and front layers often become well-trained much earlier than deep layers. To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers. Then we design Egeria, a knowledge-guided DNN training system that employs semantic knowledge from a reference model to accurately evaluate individual layers' training plasticity and safely freeze the converged ones, saving their corresponding backward computation and communication. Our reference model is generated on the fly using quantization techniques and runs forward operations asynchronously on available CPUs to minimize the overhead. In addition, Egeria caches the intermediate outputs of the frozen layers with prefetching to further skip the forward computation. Our implementation and testbed experiments with popular vision and language models show that Egeria achieves 19%-43% training speedup w.r.t. the state-of-the-art without sacrificing accuracy.

cs.LG↗

arXiv2020

A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo

In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly. The online social network (OSN) provides researchers with another perspective for detecting individuals suffering from depression. However, existing studies of depression detection based on machine learning still leave relatively low classification performance, suggesting that there is significant improvement potential for improvement in their feature engineering. In this paper, we manually build a large dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community), namely Weibo User Depression Detection Dataset (WU3D). It includes more than 20,000 normal users and more than 10,000 depressed users, both of which are manually labeled and rechecked by professionals. By analyzing the user's text, social behavior, and posted pictures, ten statistical features are concluded and proposed. In the meantime, text-based word features are extracted using the popular pretrained model XLNet. Moreover, a novel deep neural network classification model, i.e. FusionNet (FN), is proposed and simultaneously trained with the above-extracted features, which are seen as multiple classification tasks. The experimental results show that FusionNet achieves the highest F1-Score of 0.9772 on the test dataset. Compared to existing studies, our proposed method has better classification performance and robustness for unbalanced training samples. Our work also provides a new way to detect depression on other OSN platforms.

cs.SI↗