12 Papers
13 Citations
Dan-Dan Li is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Randomness & Quantum nonlocality. The author has an hindex of 5, co-authored 6 publications. Previous affiliations of Dan-Dan Li include Peking University.
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Papers
A quantum federated learning framework for classical clients
Yan-Qi Song,Yusen Wu,Shengyao Wu,Dan-Dan Li,Qiaoyan Wen,Su-Juan Qin,Fei Gao +6 more
- 18 Dec 2023
TL;DR: This paper proposes a QFL framework specifically designed for classical clients, referred to as CC-QFL, and provides valuable insights into QFL, particularly in scenarios where quantum computing resources are scarce.
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Semi-device-independent randomness expansion with partially free random sources
TL;DR: A semi-device-independent randomness expansion protocol with partially free random sources is proposed, and the condition that the partially freerandom sources should satisfy to generate new randomness is obtained.
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Security of Semi-Device-Independent Random Number Expansion Protocols.
TL;DR: The analytical relation between the amount of the generated randomness and the degree of non-classical correlation and the analytical relation under the practical conditions, where devices’ behavior is not independent and identical in each round and there exists deviation in estimating the non- classical behavior of devices are derived.
Effects of measurement dependence on generalized Clauser-Horne-Shimony-Holt Bell test in the single-run and multiple-run scenarios
TL;DR: In this paper, the authors studied the effects of relaxing the measurement independence assumption in a practical CHSH-Bell test, and established the relation among measurement dependence, guessing probability, and the maximum value of the generalized CHSH -Bell correlation function that Eve can fake.
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Improved slime mould algorithm based on Gompertz dynamic probability and Cauchy mutation with application in FJSP
Dan-Dan Li,Fei Gao +1 more
TL;DR: Wang et al. as discussed by the authors proposed a multistrategy slime mold algorithm named GCSMA for global optimization, where the Logistic-Tent double chaotic map approach is introduced to improve the quality of the initial population and a dynamic probability threshold based on Gompertz curve is designed to balance exploration and exploitation.
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