Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.0
Journals
Entropy
Special Issues
Quantum Computation, Quantum AI, and Quantum Information
entropy-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Quantum Computation, Quantum AI, and Quantum Information

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1348

Special Issue Editor

Dr. Younghun Kwon

E-Mail Website
Guest Editor
Department of Applied Physics, Hanyang University, Seoul, Republic of Korea
Interests: quantum information; quantum computation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent progress in quantum physics has significantly transformed our understanding of computation and information. The rapid development of quantum computing, quantum artificial intelligence (AI), and quantum information science is driving a new era in which quantum principles are applied to solve complex computational and informational challenges beyond classical limits. Quantum computing leverages superposition and entanglement to perform tasks with unprecedented efficiency, while quantum AI integrates these principles into machine learning and data processing, offering new possibilities for optimization and intelligent decision-making. Moreover, quantum information research deepens our knowledge of information encoding, communication, and security in quantum systems.

This Special Issue aims to highlight the most recent theoretical, experimental, and computational advances in quantum computation, quantum AI, and quantum information. We invite submissions of original research papers and comprehensive reviews covering, but not limited to, the following topics:

  • Quantum algorithms
  • Quantum computing architecture
  • Quantum computing devices
  • Quantum error correction
  • Error mitigation in quantum systems
  • Hybrid quantum–classical algorithms
  • Quantum artificial intelligence
  • Applications of quantum algorithms
  • Quantum machine learning
  • Benchmarking the performance of quantum devices
  • Quantum information theory
  • Quantum state discrimination
  • Quantum key distribution
  • Other related topics

Dr. Younghun Kwon
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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 algorithms
  • quantum computing devices
  • quantum error correction
  • quantum artificial intelligence
  • applications of quantum algorithms
  • quantum information theory
  • quantum state discrimination
  • quantum key distribution

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 389 KB  
Article
The Power of the Lorentz Quantum Computer
by Qi Zhang and Biao Wu
Entropy 2026, 28(3), 266; https://doi.org/10.3390/e28030266 - 28 Feb 2026
Cited by 1 | Viewed by 364
Abstract
We analyze the power of the recently proposed Lorentz quantum computer (LQC), a theoretical model leveraging hyperbolic bits (hybits) governed by complex Lorentz transformations. We define the complexity class BLQP (bounded-error Lorentz quantum polynomial-time) and demonstrate its equivalence to the complexity class [...] Read more.
We analyze the power of the recently proposed Lorentz quantum computer (LQC), a theoretical model leveraging hyperbolic bits (hybits) governed by complex Lorentz transformations. We define the complexity class BLQP (bounded-error Lorentz quantum polynomial-time) and demonstrate its equivalence to the complexity class P♯P (the class of problems solvable by a deterministic polynomial-time Turing machine with access to a ♯P oracle). LQC algorithms are shown to solve NP-hard problems, such as the maximum independent set (MIS), in polynomial time, thereby placing NP and co-NP within BLQP. Furthermore, we establish that LQC can efficiently simulate quantum computing with postselection (PostBQP), while the reverse is not possible, highlighting LQC’s unique “super-postselection” capability. By proving BLQP =P♯P, we situate the entire polynomial hierarchy (PH) within BLQP and reveal profound connections between computational complexity and physical frameworks like Lorentz quantum mechanics. These results underscore LQC’s theoretical superiority over conventional quantum computing models and its potential to redefine boundaries in complexity theory. Full article
(This article belongs to the Special Issue Quantum Computation, Quantum AI, and Quantum Information)
►▼ Show Figures

Figure 1

20 pages, 1275 KB  
Article
QEKI: A Quantum–Classical Framework for Efficient Bayesian Inversion of PDEs
by Jiawei Yong and Sihai Tang
Entropy 2026, 28(2), 156; https://doi.org/10.3390/e28020156 - 30 Jan 2026
Viewed by 622
Abstract
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional parameter spaces inherent in deep learning often lead to prohibitive sampling costs. Addressing this, [...] Read more.
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional parameter spaces inherent in deep learning often lead to prohibitive sampling costs. Addressing this, our work introduces Quantum-Encodable Bayesian PINNs trained via Classical Ensemble Kalman Inversion (QEKI), a framework that pairs Quantum Neural Networks (QNNs) with Ensemble Kalman Inversion (EKI). The core advantage lies in the QNN’s ability to act as a compact surrogate for PDE solutions, capturing complex physics with significantly fewer parameters than classical networks. By adopting the gradient-free EKI for training, we mitigate the barren plateau issue that plagues quantum optimization. Through several benchmarks on 1D and 2D nonlinear PDEs, we show that QEKI yields precise inversions and substantial parameter compression, even in the presence of noise. While large-scale applications are constrained by current quantum hardware, this research outlines a viable hybrid framework for including quantum features within Bayesian uncertainty quantification. Full article
(This article belongs to the Special Issue Quantum Computation, Quantum AI, and Quantum Information)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop