Advances in Quantum Key Distribution and Quantum Information

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 515

Special Issue Editors


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Guest Editor
1. State Key Laboratory of Advanced Optical Communication Systems and Networks, Center for Quantum Sensing and Information Processing, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
Interests: quantum key distribution; quantum random number generator
Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: free-space quantum key distribution; passive quantum key distribution
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Interests: quantum secret sharing; quantum artificial intelligence
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Special Issue Information

Dear Colleagues,

As the value of data gradually increases, ensuring the security of data transmission becomes crucial. Quantum cryptography utilizes the fundamental principles of quantum mechanics to generate and distribute encryption keys, providing higher security for data transmission. At present, quantum cryptography has obtained security proof at the level of information theory, and relevant industry standards are gradually being established. In addition, quantum key distribution technology has moved from laboratory verification to practical engineering applications. Specifically, the device has transitioned from discrete devices to optical integration, the transmission channel has expanded from fiber to free space, and the distribution protocol has evolved from the initial point-to-point system to multipoint-to-multipoint network architecture.

This Special Issue aims to compile a series of research papers to present the latest developments in quantum cryptography and quantum information. We sincerely invite submissions related to the theoretical and experimental progress in fields such as quantum cryptography, quantum key distribution, quantum networks, quantum chips, physical cryptography, and other fields related to quantum information.

Dr. Tao Wang
Dr. Peng Huang
Dr. Qin Liao
Guest Editors

Manuscript Submission Information

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Keywords

  • quantum key distribution
  • quantum information
  • quantum cryptography
  • quantum random number generation
  • quantum network
  • chip-based quantum devices
  • quantum noise stream cipher
  • quantum digital signatures
  • physical cryptography
  • post-quantum cryptography

Published Papers (1 paper)

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Research

14 pages, 13155 KiB  
Article
Optimizing Variational Quantum Neural Networks Based on Collective Intelligence
by Zitong Li, Tailong Xiao, Xiaoyang Deng, Guihua Zeng and Weimin Li
Mathematics 2024, 12(11), 1627; https://doi.org/10.3390/math12111627 - 22 May 2024
Viewed by 276
Abstract
Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on [...] Read more.
Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms. Full article
(This article belongs to the Special Issue Advances in Quantum Key Distribution and Quantum Information)
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