arXiv subjects

Xiao Zheng

Publications and source records attributed to Xiao Zheng.

At least 37 records · Page 2Linked to original sources

arXiv2025

$T$-equivariant disc potential and SYZ mirror construction

We develop a $G$-equivariant Lagrangian Floer theory and obtain a curved $A_\infty$ algebra, and in particular a $G$-equivariant disc potential. We construct a Morse model, which counts pearly trees in the Borel construction $L_G$. When applied to a smooth moment map fiber of a semi-Fano toric manifold, our construction recovers the $T$-equivariant toric Landau-Ginzburg mirror of Givental. We also study the $\bS^1$-equivariant Floer theory of a typical singular SYZ fiber (i.e. a pinched torus) and compute its $\bS^1$-equivariant disc potential via the gluing technique developed in \cite{CHL18,HKL}.

math.SG↗

arXiv2025

Extended dissipaton theory with application to adatom-graphene composite

In this paper, we present the extended dissipaton theory, including the dissipaton-equation-of-motion formalism and the equivalent dissipaton-embedded quantum master equation. These are exact, non-Markovian, and non-perturbative theories, capable of handling not only linear but also quadratic environmental couplings. These scenarios are prevalent in a variety of strongly correlated electronic systems, including mesoscopic nanodevices and superconductors. As a demonstration, we apply the present theory to simulate the spectral functions of an adatom on a graphene substrate. We analyze the spectral peaks in the presence of the graphene substrate and compare them to those obtained in conventional metal environments. The adatom's spectral functions reveal intricate behaviors arising from the band structure of graphene.

cond-mat.mes-hall↗

arXiv2025

A Survey on DRL based UAV Communications and Networking: DRL Fundamentals, Applications and Implementations

Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks,offering flexibility and enhanced coverage for a variety of applica-tions. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching,and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking.Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying problems. We begin by reviewing the fundamental concepts of DRL, including value-based, policy-based, and actor-critic approaches. Then,we illustrate how DRL algorithms are applied to specific UAV network tasks by discussing from problem formulations to DRL implementation. By framing UAV communication challenges as optimization problems, this survey emphasizes the practical value of DRL in dynamic and uncertain environments. We also explore the strengths of DRL in handling large-scale network scenarios and the ability to continuously adapt to changes in the environment. In addition, future research directions are outlined, highlighting the potential for DRL to further enhance UAV communications and expand its applicability to more complex,multi-agent settings.

cs.NI↗

arXiv2025

Towards Quantum Simulation of Non-Markovian Open Quantum Dynamics: A Universal and Compact Theory

Non-Markovianity, the intricate dependence of an open quantum system on its temporal evolution history, holds tremendous implications across various scientific disciplines. However, accurately characterizing the complex non-Markovian effects has posed a formidable challenge for numerical simulations. While quantum computing technologies show promise, a universal theory enabling practical quantum algorithm implementation has been elusive. We address this gap by introducing the dissipaton-embedded quantum master equation in second quantization (DQME-SQ). This exact and compact theory offers two key advantages: representability by quantum circuits and universal applicability to any Gaussian environment. We demonstrate these capabilities through digital quantum simulations of non-Markovian dissipative dynamics in both bosonic and fermionic environments. The DQME-SQ framework opens a new horizon for the efficient exploration of complex open quantum systems by leveraging the rapidly advancing quantum computing technologies.

quant-ph↗

arXiv2024

Quantum Computing in Wireless Communications and Networking: A Tutorial-cum-Survey

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such as transportation, pharmaceuticals, and industrial manufacturing,achieving unprecedented milestones. In modern society, wireless communication stands as an indispensable infrastructure, with its essence lying in optimization. Although artificial intelligence (AI) algorithms such as Reinforcement Learning (RL) and mathematical optimization have greatly enhanced the performance of wireless communication, the rapid attainment of optimal solutions for wireless communication problems remains an unresolved challenge. QC, however, presents a new alternative. This paper aims to elucidate the fundamentals of QC and explore its applications in wireless communications and networking. First, we will provide a tutorial on QC, covering its basics, characteristics, and popular QC algorithms. Next, we will introduce the applications of QC in communication and networking, followed by its applications in miscellaneous areas such as security and privacy, localization and tracking, and video streaming. Finally,we will discuss remaining open issues before concluding.

cs.NI↗

arXiv2024

HEOM-QUICK2: a general-purpose simulator for fermionic many-body open quantum systems -- An Update

Many-body open quantum systems (OQS) have a profound impact on various subdisciplines of physics, chemistry, and biology. Thus, the development of a computer program capable of accurately, efficiently, and versatilely simulating many-body OQS is highly desirable. In recent years, we have focused on the advancement of numerical algorithms based on the fermionic hierarchical equations of motion (HEOM) theory. Being in-principle exact, this approach allows for the precise characterization of many-body correlations, non-Markovian memory, and non-equilibrium thermodynamic conditions. These efforts now lead to the establishment of a new computer program, HEOM for QUantum Impurity with a Correlated Kernel, version 2 (HEOM-QUICK2), which, to the best of our knowledge, is currently the only general-purpose simulator for fermionic many-body OQS. Compared with version 1, the HEOM-QUICK2 program features more efficient solvers for stationary states, more accurate treatment of non-Markovian memory, and improved numerical stability for long-time dissipative dynamics. Integrated with quantum chemistry software, HEOM-QUICK2 has become a valuable theoretical tool for the precise simulation of realistic many-body OQS, particularly the single atomic or molecular junctions. Furthermore, the unprecedented precision achieved by HEOM-QUICK2 enables accurate simulation of low-energy spin excitations and coherent spin relaxation. The unique usefulness of HEOM-QUICK2 is demonstrated through several examples of strongly correlated quantum impurity systems under non-equilibrium conditions. Thus, the new HEOM-QUICK2 program offers a powerful and comprehensive tool for studying many-body OQS with exotic quantum phenomena and exploring applications in various disciplines.

cond-mat.str-el↗

arXiv2024

Dissipatons as generalized Brownian particles for open quantum systems: Dissipaton-embedded quantum master equation

Dissipaton theory had been proposed as an exact and nonperturbative approach to deal with open quantum system dynamics, where the influence of Gaussian environment is characterized by statistical quasi-particles named as dissipatons. In this work, we revisit the dissipaton equation of motion theory and establish an equivalent dissipatons-embedded quantum master equation (DQME), which gives rise to dissipatons as generalized Brownian particles. As explained in this work, the DQME supplies a direct approach to investigate the statistical characteristics of dissipatons and thus the physically supporting hybrid bath modes. Numerical demonstrations are carried out on the electron transfer model, exhibiting the transient statistical properties of the solvation coordinate.

quant-ph↗

arXiv2024

Theory of Electron Spin Resonance Spectroscopy in Scanning Tunneling Microscope

The integration of scanning tunneling microscopy (STM) and electron spin resonance (ESR) spectroscopy has emerged as a powerful and innovative tool for discerning spin excitations and spin-spin interactions within atoms and molecules adsorbed on surfaces. However, the origin of the STM-ESR signal and the underlying mechanisms that govern the essential features of the measured spectra have remained elusive, thereby significantly impeding the future development of the STM-ESR approach. Here, we construct a model to carry out precise numerical simulations of STM-ESR spectra for a single hydrogenated Ti adatom and a hydrogenated Ti dimer, achieving excellent agreement with experimental observations. We further develop an analytic theory that elucidates the fundamental origin of the signal as well as the essential features in the measured spectra. These new theoretical developments establish a solid foundation for the on-demand detection and manipulation of atomic-scale spin states, with promising implications for cutting-edge applications in spin sensing, quantum information, and quantum computing.

cond-mat.mes-hall↗

arXiv2024

Location-Driven Beamforming for RIS-Assisted Near-Field Communications

Future wireless communications are promising to support ubiquitous connections and high data rates with cost-effective devices. Benefiting from the energy-efficient elements with low cost, reconfigurable intelligent surface (RIS) emerges as a potential solution to fulfill such demands, which has the capability to flexibly manipulate the wireless signals with a tunable phase. Recently, as the operational frequency ascends to the sub-terahertz (THz) bands or higher bands for wireless communications in six-generation (6G), it becomes imperative to consider the near-field propagation in RIS-assisted communications. The challenging acquisition of channel parameters is an inherent issue for near-field RIS-assisted communications, where the complex design is essential to acquire the informative near-field channel embedded with both the angle and distance information. Hence, in this paper we systematically investigate the potential of exploiting location information for near-field RIS-assisted communications. Firstly, we present the progresses in the near-field RIS-assisted communications, which are compatible with existing wireless communications and show the potential to achieve the fine-grained localization accuracy to support location-driven scheme. Then, the Fresnel zone based model is introduced, with which the location-driven beamforming scheme and corresponding frame structure are developed. Also, we elaborate on four unique advantages for leveraging location information in RIS-assisted communications, followed by numerical simulations. Finally, several key challenges and corresponding potential solutions are pointed out.

cs.IT↗

arXiv2024

A Near-Optimal Category Information Sampling in RFID Systems

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Then, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments validate the practicality of OPT-C.

cs.NI↗

arXiv2024

Huygens-Fresnel Model Based Position-Aided Phase Configuration for 1-Bit RIS Assisted Wireless Communication

Reconfigurable intelligent surface (RIS), composed of nearly passive elements, is regarded as one of the potential paradigms to support multi-gigabit data in real-time. However, in traditional CSI (channel state information) driven frame, the training overhead of channel estimation greatly increases as the number of RIS elements increases to intelligently manipulate the reflected signals. To conveniently use the reflected signal without complex CSI feedback, in this paper we propose a position-aided phase configuration scheme based on the property of Fresnel zone. In particular, we design the impedance based discrete RIS elements with joint absorption mode and reflection mode considering the fabrication complexities, which integrated the property of the Fresnel zone to resist the impact of position error. Then, with joint absorption and 1-bit reflection mode elements, we develop the two-step position-aided ON/OFF states judgement (TPOSJ) scheme and the frame structure to control the ON/OFF state of RIS, followed by analyzing the impacts of mobility and position error on our proposed scheme. Also, we derive the Helmholtz-Kirchhoff integral theorem based power flow. Simulations show that the proposed scheme can manipulate the ON/OFF state intelligently without complex CSI, thus verifying the practical application of our proposed scheme.

cs.IT↗

arXiv2024

Disk potential functions for polygon spaces

We derive a Floer theoretical SYZ mirror for an equilateral and generic polygon space. The disk potential function of the monotone torus fiber of the caterpillar bending system is calculated by computing non-trivial open Gromov--Witten invariants from the structural result of the monotone Fukaya category, the topology of fibers of completely integrable systems, and toric degenerations. Then, combining the result with the work of Nohara--Ueda [NU20] and Marsh--Rietsch [MR20], we obtain the disk potential functions of bending systems and produce a mirror cluster variety of type A without frozen variables via Lagrangian Floer theory.

math.SG↗

arXiv2023

Point Cloud Pre-training with Diffusion Models

Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However, due to the unordered and non-uniform density characteristics of point clouds, it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper, we propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif). We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy, which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection. Specifically, PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore, our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains.

cs.CV↗

arXiv2023

Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System

Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services. Despite its benefits, ensuring the reliability and safety of a microservice system remains highly challenging. Existing anomaly detection algorithms based on a single data modality (i.e., metrics, logs, or traces) fail to fully account for the complex correlations and interactions between different modalities, leading to false negatives and false alarms, whereas incorporating more data modalities can offer opportunities for further performance gain. As a fresh attempt, we propose in this paper a semi-supervised graph-based anomaly detection method, MSTGAD, which seamlessly integrates all available data modalities via attentive multi-modal learning. First, we extract and normalize features from the three modalities, and further integrate them using a graph, namely MST (microservice system twin) graph, where each node represents a service instance and the edge indicates the scheduling relationship between different service instances. The MST graph provides a virtual representation of the status and scheduling relationships among service instances of a real-world microservice system. Second, we construct a transformer-based neural network with both spatial and temporal attention mechanisms to model the inter-correlations between different modalities and temporal dependencies between the data points. This enables us to detect anomalies automatically and accurately in real-time. The source code of MSTGAD is publicly available at https://github.com/alipay/microservice_system_twin_graph_based_anomaly_detection.

cs.LG↗

arXiv2023

DxPU: Large Scale Disaggregated GPU Pools in the Datacenter

The rapid adoption of AI and convenience offered by cloud services have resulted in the growing demands for GPUs in the cloud. Generally, GPUs are physically attached to host servers as PCIe devices. However, the fixed assembly combination of host servers and GPUs is extremely inefficient in resource utilization, upgrade, and maintenance. Due to these issues, the GPU disaggregation technique has been proposed to decouple GPUs from host servers. It aggregates GPUs into a pool, and allocates GPU node(s) according to user demands. However, existing GPU disaggregation systems have flaws in software-hardware compatibility, disaggregation scope, and capacity. In this paper, we present a new implementation of datacenter-scale GPU disaggregation, named DxPU. DxPU efficiently solves the above problems and can flexibly allocate as many GPU node(s) as users demand. In order to understand the performance overhead incurred by DxPU, we build up a performance model for AI specific workloads. With the guidance of modeling results, we develop a prototype system, which has been deployed into the datacenter of a leading cloud provider for a test run. We also conduct detailed experiments to evaluate the performance overhead caused by our system. The results show that the overhead of DxPU is less than 10%, compared with native GPU servers, in most of user scenarios.

cs.DC↗

arXiv2023

Stronger Reverse Uncertainty Relation for Multiple Incompatible Observables

Recently,D.Mondal et.al[Phys. Rev. A. 95, 052117(2017)]creatively introduce a new interesting concept of reverse uncertainty relation which indicates that one cannot only prepare quantum states with joint small uncertainty, but also with joint great uncertainty for incompatible observables. However, the uncertainty upper bound they constructed cannot express the essence of this concept well, i.e., the upper bound will go to infinity in some cases even for incompatible observables. Here, we construct a new reverse uncertainty relation and successfully fix this "infinity" problem. Also, it is found that the reverse uncertainty relation and the normal uncertainty relation are the same in essential, and they both can be unified by the same theoretical framework. Moreover, taking advantage of this unified framework, one can construct a reverse uncertainty relation for multiple observables with any tightness required. Meanwhile, the application of the new uncertainty relation in purity detection is discussed.

quant-ph↗

arXiv2023

Dynamics Investigation of the quantum-control-assisted multipartite uncertainty relation in Heisenberg model with Dzyaloshinski-Moriya interaction

Recently, Zheng constructs a quantum-control-assisted multipartite variance-based uncertainty relation, which successfully extends the conditional uncertainty relation to the multipartite case [Annalen der physik, 533, 2100014 (2021)]. We here investigate the dynamics of the new uncertainty relation in the Heisenberg system with the Dzyaloshinski-Moriya interaction. It is found that, different from entanglement, the mixedness of the system has an interesting single-valued relationship with the tightness and lower bound of the uncertainty relation. This single-valued relationship indicates that the tightness and lower bound of the uncertainty relation can be written as the functional form of the mixedness. Moreover, the single-valued relationship with the mixedness is the common nature of conditional uncertainty relations, and has no relationship with the form of the uncertainty relations. Also, the comparison between the new conditional variance-based uncertainty relation and the existing entropic one has been made.

quant-ph↗

arXiv2023

Extended dissipaton equation of motion for electronic open quantum systems: Application to the Kondo impurity model

In this paper, we present an extended dissipaton equation of motion for studying the dynamics of electronic impurity systems. Compared with the original theoretical formalism, the quadratic couplings are introduced into the Hamiltonian accounting for the interaction between the impurity and its surrounding environment. By exploiting the quadratic fermionic dissipaton algebra, the proposed extended dissipaton equation of motion offers a powerful tool for studying the dynamical behaviors of electronic impurity systems, particularly in situations where nonequilibrium and strongly correlated effects play significant roles. Numerical demonstrations are carried out to investigate the temperature dependence of the Kondo resonance in the Kondo impurity model.

cond-mat.str-el↗