arXiv subjects

Qiying Pan

Publications and source records attributed to Qiying Pan.

8 recordsLinked to original sources

arXiv2026

Entropy and Variance Squeezing of V-type Atom in Dissipative Cavity

Based on Ref.\cite{Riccardi A}, we investigate the entropy and variance squeezing of a V-type atom in a dissipative cavity, and discuss the influences of parameters including the spontaneously generated interference (SGI) ($\theta$), the cavity-environment coupling ($\gamma_0/\kappa$) and the atom-cavity detuning ($\Delta$) on the atomic squeezing by using different initial states. The results show that no squeezing of $S_y$ occurs under any condition and that variance squeezing of $S_x$ appears only when $\Delta>0$. Entropy squeezing quantifies quantum fluctuations more precisely than variance squeezing. Moreover, the atomic squeezing of $S_x$ clearly depends on $\theta$, $\gamma_0/\kappa$, $\Delta$ and the initial state. These findings are meaningful for quantum information processing as an ultra-low-noise resource.

quant-ph↗

arXiv2025

Controlling Quantum Coherence of V-type Atom in Dissipative Cavity by Detuning and Weak Measurement Reversal

In this work, an interactive system composed of a V-type atom and a dissipative single-mode cavity is considered and the atomic quantum coherences are investigated under parameters including spontaneously generated interference (SGI), cavity-environment coupling, weak measurement and its reversal, and detuning between the atom and the cavity. The results indicate that, the strong coupling can induce coherence sudden death (CSD) and coherence sudden birth (CSB), and the non-zero SGI parameter only induces CSB but the detuning may avoid CSD and CSB. Moreover, detuning and weak measurement reversal can very effectively protect quantum coherence, while the SGI parameter, weak measurement, and strong coupling can accelerate its attenuation. The SGI parameter, detuning, weak measurement reversal, and strong coupling all promote the generation of coherence, whereas weak measurement alone can suppress it. In particular, the maximal coherent state can be very effectively protected and the coherent state can be prepared if all parameters are selected appropriately. Physical interpretations are also provided for these results.

quant-ph↗

arXiv2024

SatFlow: Scalable Network Planning for LEO Mega-Constellations

Low-earth-orbit (LEO) satellite communication networks have evolved into mega-constellations with hundreds to thousands of satellites inter-connecting with inter-satellite links (ISLs). Network planning, which plans for network resources and architecture to improve the network performance and save operational costs, is crucial for satellite network management. However, due to the large scale of mega-constellations, high dynamics of satellites, and complex distribution of real-world traffic, it is extremely challenging to conduct scalable network planning on mega-constellations with high performance. In this paper, we propose SatFlow, a distributed and hierarchical network planning framework to plan for the network topology, traffic allocation, and fine-grained ISL terminal power allocation for mega-constellations. To tackle the hardness of the original problem, we decompose the grand problem into two hierarchical sub-problems, tackled by two-tier modules. A multi-agent reinforcement learning approach is proposed for the upper-level module so that the overall laser energy consumption and ISL operational costs can be minimized; A distributed alternating step algorithm is proposed for the lower-level module so that the laser energy consumption could be minimized with low time complexity for a given topology. Extensive simulations on various mega-constellations validate SatFlow's scalability on the constellation size, reducing the flow violation ratio by up to 21.0% and reducing the total costs by up to 89.4%, compared with various state-of-the-art benchmarks.

cs.NI↗

arXiv2024

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices

Graph neural networks (GNN) have been widely deployed in real-world networked applications and systems due to their capability to handle graph-structured data. However, the growing awareness of data privacy severely challenges the traditional centralized model training paradigm, where a server holds all the graph information. Federated learning is an emerging collaborative computing paradigm that allows model training without data centralization. Existing federated GNN studies mainly focus on systems where clients hold distinctive graphs or sub-graphs. The practical node-level federated situation, where each client is only aware of its direct neighbors, has yet to be studied. In this paper, we propose the first federated GNN framework called Lumos that supports supervised and unsupervised learning with feature and degree protection on node-level federated graphs. We first design a tree constructor to improve the representation capability given the limited structural information. We further present a Monte Carlo Markov Chain-based algorithm to mitigate the workload imbalance caused by degree heterogeneity with theoretically-guaranteed performance. Based on the constructed tree for each client, a decentralized tree-based GNN trainer is proposed to support versatile training. Extensive experiments demonstrate that Lumos outperforms the baseline with significantly higher accuracy and greatly reduced communication cost and training time.

cs.LG↗

arXiv2024

On provable privacy vulnerabilities of graph representations

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks. Our research primarily addresses the theoretical underpinnings of similarity-based edge reconstruction attacks (SERA), furnishing a non-asymptotic analysis of their reconstruction capacities. Moreover, we present empirical corroboration indicating that such attacks can perfectly reconstruct sparse graphs as graph size increases. Conversely, we establish that sparsity is a critical factor for SERA's effectiveness, as demonstrated through analysis and experiments on (dense) stochastic block models. Finally, we explore the resilience of private graph representations produced via noisy aggregation (NAG) mechanism against SERA. Through theoretical analysis and empirical assessments, we affirm the mitigation of SERA using NAG . In parallel, we also empirically delineate instances wherein SERA demonstrates both efficacy and deficiency in its capacity to function as an instrument for elucidating the trade-off between privacy and utility.

cs.LG↗

arXiv2024

Modulating Entanglement Dynamics of Two V-type Atoms in Dissipative Cavity by Detuning, Weak Measurement and Reversal

In this paper, how to modulate entanglement dynamics of two V-type atoms in dissipative cavity by detuning, weak measurement and weak measurement reversal is studied. The analytical solution of this model is obtained by solving Schrodinger Equation after diagonalizing Hamiltonian of dissipative cavity. It is discussed in detail how the entanglement dynamics is influenced by cavity--environment coupling, spontaneously generated interference (SGI) parameter, detuning between cavity with environment and weak measurement reversal. The results show that the entanglement dynamics of different initial states obviously depends on coupling, SGI parameter, detuning and reversing measurement strength. The stronger coupling, the smaller SGI parameter, the larger detuning and the bigger reversing measurement strength can all not only protect but also generate the entanglement, and the detuning is more effectively in tne strong coupling regime than the weak measurement reversal, which is more effectively than the SGI parameter. We also give corresponding physical interpretations.

quant-ph↗

arXiv2023

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks

Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the problem by representing local tasks using different statistics and relating them through a simple aggregation mechanism. However, these approaches suffer from limited efficiency from two aspects: low quality of task-relatedness quantification and inefficacy of exploiting the collaboration structure. To address these issues, we propose FedGKD, a novel federated GNN framework that utilizes a novel client-side graph dataset distillation method to extract task features that better describe task-relatedness, and introduces a novel server-side aggregation mechanism that is aware of the global collaboration structure. We conduct extensive experiments on six real-world datasets of different scales, demonstrating our framework's outperformance.

cs.LG↗

arXiv2022

FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy

Node embedding aims to map nodes in the complex graph into low-dimensional representations. The real-world large-scale graphs and difficulties of labeling motivate wide studies of unsupervised node embedding problems. Nevertheless, previous effort mostly operates in a centralized setting where a complete graph is given. With the growing awareness of data privacy, data holders who are only aware of one vertex and its neighbours demand greater privacy protection. In this paper, we introduce FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk is designed to offer centralized competitive graph representation capability with data privacy protection and great communication efficiency. FedWalk instantiates the prevalent federated paradigm and contains three modules. We first design a hierarchical clustering tree (HCT) constructor to extract the structural feature of each node. A dynamic time warping algorithm seamlessly handles the structural heterogeneity across different nodes. Based on the constructed HCT, we then design a random walk generator, wherein a sequence encoder is designed to preserve privacy and a two-hop neighbor predictor is designed to save communication cost. The generated random walks are then used to update node embedding based on a SkipGram model. Extensive experiments on two large graphs demonstrate that Fed-Walk achieves competitive representativeness as a centralized node embedding algorithm does with only up to 1.8% Micro-F1 score and 4.4% Marco-F1 score loss while reducing about 6.7 times of inter-device communication per walk.

cs.DC↗