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Entropy
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Quantum Information and Quantum Computation
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Quantum Information and Quantum Computation

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 1339

Special Issue Editors

Dr. Zhian Jia

E-Mail Website
Guest Editor
Centre for Quantum Technologies, National University of Singapore, Singapore 119077, Singapore
Interests: generalized symmetry; quantum field theory; quantum information theory; quantum computation theory; topological order; mathematical physics
Dr. Dagomir Kaszlikowski

E-Mail Website
Guest Editor
Centre for Quantum Technologies, National University of Singapore, Singapore 119077, Singapore
Interests: quantum foundations; quantum contextuality; generalized probabilistic theory; quantum information theory; quantum computation theory

Special Issue Information

Dear Colleagues,

Quantum information and computation theory have undergone remarkable development over the past several decades. They have not only deepened our understanding of quantum mechanics but also found numerous applications in the real world.

From a foundational perspective, the introduction of concepts such as entanglement, steering, nonlocality, and quantum discord, alongside the discovery of novel phenomena, has significantly advanced our comprehension of quantum correlations. These insights have had profound implications for various domains, including quantum computation, quantum metrology, quantum communication, quantum cryptography, condensed matter physics, and quantum field theory.

This Special Issue serves as a platform for showcasing new and improved techniques in quantum information and computation theory, fostering the continued growth and innovation in this vibrant field.

Dr. Zhian Jia
Dr. Dagomir Kaszlikowski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum foundations
  • quantum games
  • generalized probabilistic theory
  • quantum computation
  • quantum algorithm
  • quantum simulation
  • quantum metrology
  • quantum entanglement, steering, and nonlocality
  • quantum contextuality
  • topological quantum information
  • quantum error correction
  • quantum memory

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

27 pages, 958 KiB  
Article
AQEA-QAS: An Adaptive Quantum Evolutionary Algorithm for Quantum Architecture Search
by Yaochong Li, Jing Zhang, Rigui Zhou, Yi Qu and Ruiqing Xu
Entropy 2025, 27(7), 733; https://doi.org/10.3390/e27070733 - 8 Jul 2025
Viewed by 306
Abstract
Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays [...] Read more.
Quantum neural networks (QNNs) represent an emerging technology that uses a quantum computer for neural network computations. The QNNs have demonstrated potential advantages over classical neural networks in certain tasks. As a core component of a QNN, the parameterized quantum circuit (PQC) plays a crucial role in determining the QNN’s overall performance. However, quantum circuit architectures designed manually based on experience or using specific hardware structures can suffer from inefficiency due to the introduction of redundant quantum gates, which amplifies the impact of noise on system performance. Recent studies have suggested that the advantages of quantum evolutionary algorithms (QEAs) in terms of precision and convergence speed can provide an effective solution to quantum circuit architecture-related problems. Currently, most QEAs adopt a fixed rotation mode in the evolution process, and a lack of an adaptive updating mode can cause the QEAs to fall into a local optimum and make it difficult for them to converge. To address these problems, this study proposes an adaptive quantum evolution algorithm (AQEA). First, an adaptive mechanism is introduced to the evolution process, and the strategy of combining two dynamic rotation angles is adopted. Second, to prevent the fluctuations of the population’s offspring, the elite retention of the parents is used to ensure the inheritance of good genes. Finally, when the population falls into a local optimum, a quantum catastrophe mechanism is employed to break the current population state. The experimental results show that compared with the QNN structure based on manual design and QEA search, the proposed AQEA can reduce the number of network parameters by up to 20% and increase the accuracy by 7.21%. Moreover, in noisy environments, the AQEA-optimized circuit outperforms traditional circuits in maintaining high fidelity, and its excellent noise resistance provides strong support for the reliability of quantum computing. Full article
(This article belongs to the Special Issue Quantum Information and Quantum Computation)
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17 pages, 1566 KiB  
Article
A Method Inspired by One-Dimensional Discrete-Time Quantum Walks for Influential Node Identification
by Wen Liang, Yifan Wang, Qiwei Liu and Wenbo Zhang
Entropy 2025, 27(6), 634; https://doi.org/10.3390/e27060634 - 14 Jun 2025
Viewed by 252
Abstract
Identifying influential nodes in complex networks is essential for a wide range of applications, from social network analysis to enhancing infrastructure resilience. While quantum walk-based methods offer potential advantages, existing approaches face challenges in dimensionality, computational efficiency, and accuracy. To address these limitations, [...] Read more.
Identifying influential nodes in complex networks is essential for a wide range of applications, from social network analysis to enhancing infrastructure resilience. While quantum walk-based methods offer potential advantages, existing approaches face challenges in dimensionality, computational efficiency, and accuracy. To address these limitations, this study proposes a novel method inspired by the one-dimensional discrete-time quantum walk (IOQW). This design enables the development of a simplified shift operator that leverages both self-loops and the network’s structural connectivity. Furthermore, degree centrality and path-based features are integrated into the coin operator, enhancing the accuracy and scalability of the IOQW framework. Comparative evaluations against state-of-the-art quantum and classical methods demonstrate that IOQW excels in capturing both local and global topological properties while maintaining a low computational complexity of O(N⟨k⟩), where ⟨k⟩ denotes the average degree. These advancements establish IOQW as a powerful and practical tool for influential node identification in complex networks, bridging quantum-inspired techniques with real-world network science applications. Full article
(This article belongs to the Special Issue Quantum Information and Quantum Computation)
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13 pages, 820 KiB  
Article
An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code
by Yuan-Hang Zhang, Zhian Jia, Yu-Chun Wu and Guang-Can Guo
Entropy 2025, 27(6), 627; https://doi.org/10.3390/e27060627 - 13 Jun 2025
Viewed by 385
Abstract
Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. In this work, we present an analytical proof that RBMs can exactly and efficiently represent stabilizer code states—a class of highly entangled quantum states [...] Read more.
Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. In this work, we present an analytical proof that RBMs can exactly and efficiently represent stabilizer code states—a class of highly entangled quantum states that are central to quantum error correction. Given a set of stabilizer generators, we develop an efficient algorithm to determine both the RBM architecture and the exact values of its parameters. Our findings provide new insights into the expressive power of RBMs, highlighting their capability to encode highly entangled states, and may serve as a useful tool for the classical simulation of quantum error-correcting codes. Full article
(This article belongs to the Special Issue Quantum Information and Quantum Computation)
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