Zhiwei Sun

Privacy-preserving collective reinforcement learning using fully homomorphic encryption in usage-based insurance

Xiaoqiang Sun, Zhiwei Sun, Ting Wang, Guangwu Hu et autres

cn (code pays fourni par la source)

Federated Multi-View Unsupervised Feature Selection via Bio-Inspired Hierarchical-Cognitive Tianji’s Horse Racing Optimization and Tensor Learning

Rong Cheng, Zhiwei Sun, Kun Qi, Wangyu Wu et autres

As multi-view datasets expand across diverse practical fields, feature selection (FS) has become an indispensable preparatory stage for machine learning models. Nevertheless, real-world multi-view data is often unlabeled and distributed among isolated clients, posing significant challenges to traditional centralized methods due to …

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Entangled qutrit states from simple graphs

慧 何, Duanfeng Liu, Zenan Ye, Zhiwei Sun

Abstract This paper presents a theoretical framework for generating and controlling high-dimensional quantum entanglement by harnessing the scattering properties of quantum graphs (QGs). By mapping the three scattering channels of a QG onto the basis states of a quantum three-level system (qutrit), …

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Improvements on Efficient Controlled Semi‐Quantum Secret Sharing With Entangled States

Cai Zhang, Ruiqing Zhang, Run-hua Tang, Zhiwei Sun

ABSTRACT Semi‐quantum secret sharing represents a significant area in quantum cryptography, offering the advantage of not requiring all participants to be fully quantum‐capable. Recently, Houshmand et al. (Opt. Quant. Electron. 2024, 56(5), 759) proposed a controlled semi‐quantum secret sharing protocol based on …

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Privacy-Preserving Intelligence-based Reinforcement Learning for Large Language Model via Homomorphic Encryption

Feiyang Wu, Xiaoqiang Sun, Zhiwei Sun, Wei Liu et autres

Reinforcement learning (RL) is a widely used framework for sequential decision-making, but conventional RL often struggles with sparse rewards, limited reasoning, and long-horizon dependencies. Intelligence-based reinforcement learning (IRL) introduces intelligence-oriented metrics and is mainly applied to large language models (LLMs) to enhance …

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