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相关论文: On the analytical convergence of the QPA procedure

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Quantum state privacy amplification (QSPA) is the quantum analogue of classical privacy amplification. If the state information of a series of single particle states has some leakage, QSPA reduces this leakage by condensing the state…

量子物理 · 物理学 2010-07-20 Liang Hao , Chuan Wang , Gui Lu Long

Quantum purity amplification (QPA) is the task of coherently transforming $n$ copies of a mixed state into high-fidelity copies of a chosen eigenstate. We solve QPA in the general setting of $n$ input copies, $m$ output copies, arbitrary…

量子物理 · 物理学 2026-05-22 Zhaoyi Li , Elias Theil , Aram W. Harrow , Isaac Chuang

Existing quantum cryptographic schemes are not, as they stand, operable in the presence of noise on the quantum communication channel. Although they become operable if they are supplemented by classical privacy-amplification techniques, the…

量子物理 · 物理学 2009-01-23 D. Deutsch , A. Ekert , R. Jozsa , C. Macchiavello , S. Popescu , A. Sanpera

We show that three principle means of treating privacy amplification in quantum key distribution, private state distillation, classical privacy amplification, and via the uncertainty principle, are equivalent and interchangeable. By…

量子物理 · 物理学 2013-05-29 Joseph M. Renes , Jean-Christian Boileau

Quantum purity amplification (QPA) provides a novel approach to counteracting the pervasive noise that degrades quantum states. We present the optimal QPA protocol for general quantum systems and global noise, resolving a two-decade open…

量子物理 · 物理学 2025-12-29 Zhaoyi Li , Honghao Fu , Takuya Isogawa , Caio Silva , Isaac Chuang

Differential privacy provides a theoretical framework for processing a dataset about $n$ users, in a way that the output reveals a minimal information about any single user. Such notion of privacy is usually ensured by noise-adding…

量子物理 · 物理学 2023-08-23 Armando Angrisani , Mina Doosti , Elham Kashefi

Privacy amplification (PA) is an essential part in a quantum key distribution (QKD) system, distilling a highly secure key from a partially secure string by public negotiation between two parties. The optimization objectives of privacy…

量子物理 · 物理学 2021-06-08 Yan Bingze , Li Qiong , Mao Haokun , Chen Nan

Differential privacy comes equipped with multiple analytical tools for the design of private data analyses. One important tool is the so-called "privacy amplification by subsampling" principle, which ensures that a differentially private…

机器学习 · 计算机科学 2018-11-26 Borja Balle , Gilles Barthe , Marco Gaboardi

We show that the tasks of privacy amplification against quantum adversaries and data compression with quantum side information are dual in the sense that the ability to perform one implies the ability to perform the other. These are two of…

量子物理 · 物理学 2011-06-10 Joseph M. Renes

Privacy amplification is the art of shrinking a partially secret string Z to a highly secret key S. We show that, even if an adversary holds quantum information about the initial string Z, the key S obtained by two-universal hashing is…

量子物理 · 物理学 2007-05-23 Renato Renner , Robert Koenig

Standard quantum inference converts quantum data into classical outputs. We study an alternative inference setting in which the desired output is quantum, preserving coherence. Such settings include quantum purity amplification (QPA),…

量子物理 · 物理学 2026-05-21 Zhaoyi Li , Elias Theil , Aram W. Harrow , Isaac Chuang

Privacy amplification is an indispensable step in postprocessing of continuous-variable quantum key distribution (CV-QKD), which is used to distill unconditional secure keys from identical corrected keys between two distant legal parties.…

量子物理 · 物理学 2018-05-08 Xiangyu Wang , Yi-Chen Zhang , Song Yu , Hong Guo

Quantum-limited amplifiers increase the amplitude of quantum signals at the price of introducing additional noise. Quantum purification protocols operate in the reverse way, by reducing the noise while attenuating the signal. Here we…

量子物理 · 物理学 2017-04-12 Xiaobin Zhao , Giulio Chiribella

We consider privacy amplification against quantum side information by using regular random binning as an effective extractor. For constant-type sources, we obtain error exponent and strong converse bounds in terms of the so-called quantum…

量子物理 · 物理学 2023-09-21 Yu-Chen Shen , Li Gao , Hao-Chung Cheng

Privacy amplification (PA) is an indispensable component in classical and quantum cryptography. Error correction (EC) and data compression (DC) algorithms are also indispensable in classical and quantum information theory. We here study…

量子物理 · 物理学 2022-02-01 Toyohiro Tsurumaru

Privacy amplification is the key step to guarantee the security of quantum communication. The existing security proofs require accumulating a large number of raw key bits for privacy amplification. This is similar to block ciphers in…

量子物理 · 物理学 2022-07-05 Yizhi Huang , Xingjian Zhang , Xiongfeng Ma

Privacy amplification (PA) is an essential post-processing step in quantum key distribution (QKD) for removing any information an eavesdropper may have on the final secret key. In this paper, we consider delaying PA of the final key after…

量子物理 · 物理学 2012-03-12 Chi-Hang Fred Fung , Xiongfeng Ma , H. F. Chau , Qing-yu Cai

Privacy amplification is an indispensable step in the post-processing of quantum key distribution, which can be used to compress the redundancy of shared key and improve the security level of the key. The commonly used privacy amplification…

量子物理 · 物理学 2021-09-16 Wei Li , Shengmei Zhao

High-quality, distributed quantum entanglement is the distinctive resource for quantum communication and forms the foundation for the unequalled level of security that can be assured in quantum key distribution. While the entanglement…

量子物理 · 物理学 2024-08-23 Philipp Sohr , Sebastian Ecker , Lukas Bulla , Martin Bohmann , Rupert Ursin

Recent research in differential privacy demonstrated that (sub)sampling can amplify the level of protection. For example, for $\epsilon$-differential privacy and simple random sampling with sampling rate $r$, the actual privacy guarantee is…

应用统计 · 统计学 2022-02-22 Jingchen Hu , Joerg Drechsler , Hang J. Kim
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