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Advances in Quantum Information Processing: Theory, Methods and Emerging Applications
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Advances in Quantum Information Processing: Theory, Methods and Emerging Applications

A Special Issue of Information (ISSN 2078-2489) belonging to the section "Information Theory and Methodology".

Deadline for manuscript submissions: 31 July 2027 | Viewed by 3843

Editors

Dr. Wenbin Yu

E-Mail Website
Guest Editor
School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: quantum information processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yadang Chen

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: continuous parameters; initialization; prompt learning; trigger token
Dr. Chengjun Zhang

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: complex networks; information filtering; deep Learning

Special Issue Information

Dear Colleagues,

Quantum Information Processing (QIP) has evolved from foundational studies of qubits and quantum algorithms into a broad interdisciplinary field encompassing continuous-variable systems, hybrid quantum–classical architectures, and domain-specific applications such as secure communication, quantum sensing, and quantum-enhanced optimization. The second volume of this Special Issue seeks cutting-edge contributions that push the boundaries of theory, advance experimental implementations, and demonstrate real-world impacts across science and engineering.

Topics of Interest:

We invite original research articles and high-quality reviews in, but not limited to, the following areas:

  • Quantum Algorithms and Complexity: Novel algorithms, complexity analyses, and quantum-speedup benchmarks;
  • Error Correction and Fault Tolerance: New codes, fault-tolerant architectures, and error-suppression techniques;
  • Continuous-Variable and Hybrid Systems: Gaussian protocols, non-Gaussian resources, and hybrid discrete–continuous schemes;
  • Quantum Communication and Key Distribution: Next-generation QKD protocols, network architectures, and security proofs;
  • Quantum Machine Learning and Optimization: Variational circuits, QUBO formalisms, and domain-specific heuristics;
  • Emerging Applications: Quantum sensing, imaging, healthcare, finance, energy systems, and beyond.

Dr. Wenbin Yu
Dr. Yadang Chen
Dr. Chengjun Zhang
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 1800 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 machine learning
  • quantum information
  • quantum computation
  • quantum communication
  • machine learning

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Related Special Issue

Published Papers (4 papers)

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Research

18 pages, 435 KB  
Article
Numerical Search for Extensions of Tensor-Product Mutually Unbiased Bases in Non-Prime-Power Composite Dimensions up to 100
by Jeffery Wu, Ziyuan Liu and Shengjun Wu
Information 2026, 17(8), 796; https://doi.org/10.3390/info17080796 - 19 Aug 2026
Viewed by 151
Abstract
For every prime-power dimension, a complete set of d+1 mutually unbiased bases (MUBs) is known, but in non-prime-power composite dimensions, the maximum number N(d) remains open. The tensor-product construction supplies [...] Read more.
For every prime-power dimension, a complete set of d+1 mutually unbiased bases (MUBs) is known, but in non-prime-power composite dimensions, the maximum number N(d) remains open. The tensor-product construction supplies mini(piai)+1 MUBs for d=∏ipiai, and this is the best lower bound currently known for every non-prime-power composite d≤100. We do not attempt to determine N(d); we ask whether these specific tensor-product sets admit one additional basis. We build and verify the sets for all 64 such dimensions, with pairwise overlap deviations below 10−15; run a construction-free joint search for d≤7; compare twelve optimizers; and test construction, convergence, and success thresholds. For a specified protocol A—algorithm, initialization distribution, success criterion, and stopping rule—let qA(d) denote the probability that one descent recovers a provably existing extension. Among the successful local-descent protocols tested, the inferred probabilities have comparable order, whereas the number of descents per computational budget differs much more strongly. With maxfun unbounded, exact gradients and optimization on U(d) recover guaranteed extensions at d=10,12, and 16. Raising the Riemannian CG iteration limit from 6000 to 150,000 changes none of the success counts or extension-search summaries. In d=6, the numerical failure to extend the tensor-product-basis triple reproduces a known analytic unextendibility theorem; it does not resolve the general four-MUB problem. Moreover, the actual extension target is maximally entangled. Haar-random initialization has zero probability of lying exactly on that structured submanifold, and the unrestricted search has not been validated for convergence to it, so recovery probabilities from the tensor-basis validation task cannot be transferred directly to the extension problem. The constructed bases, numerical summaries, per-restart arrays for the optimizer and threshold studies, complete per-descent arrays, and all code are publicly archived. Full article
(This article belongs to the Special Issue Advances in Quantum Information Processing: Theory, Methods and Emerging Applications)
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14 pages, 1058 KB  
Article
QCNN-Inspired Variational Circuits for Enhanced Noise Robustness in Quantum Deep Q-Learning
by Louyang Yu, Wenbin Yu, Yadang Chen and Chengjun Zhang
Information 2026, 17(3), 250; https://doi.org/10.3390/info17030250 - 3 Mar 2026
Viewed by 622
Abstract
Quantum reinforcement learning (QRL) is often evaluated under idealized, noiseless assumptions, yet realistic quantum devices inevitably introduce noise that can severely degrade performance. This paper improves the robustness of quantum deep Q-learning (QDQN) by redesigning the variational quantum circuit (VQC) used in its [...] Read more.
Quantum reinforcement learning (QRL) is often evaluated under idealized, noiseless assumptions, yet realistic quantum devices inevitably introduce noise that can severely degrade performance. This paper improves the robustness of quantum deep Q-learning (QDQN) by redesigning the variational quantum circuit (VQC) used in its value-function approximator. Motivated by recent advances in quantum convolutional neural networks (QCNNs), we construct four QCNN-inspired VQC variants (Models A–D) by combining representative QCNN two-qubit building blocks with an explicit fully connected (all-to-all) layer. Using a 10-fold evaluation protocol at a fixed noise level p = 0.005, Model D achieves the best robustness, reducing the mean number of episodes required to reach a target reward from 1981 (baseline) to 1243. Under a stricter success criterion, Model D also doubles the empirically observed noise-tolerance boundary from 0.002 to 0.004. These results indicate that carefully chosen QCNN-style circuit components and connectivity can significantly improve the noise robustness of QDQN-like QRL agents. Full article
(This article belongs to the Special Issue Advances in Quantum Information Processing: Theory, Methods and Emerging Applications)
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7 pages, 236 KB  
Article
Topological Formalism for Quantum Entanglement via B3 and S0 Mappings
by Sergio Manzetti
Information 2025, 16(11), 997; https://doi.org/10.3390/info16110997 - 17 Nov 2025
Viewed by 802
Abstract
We present two propositions and a theorem to establish a foundational framework for a novel perspective on quantum information framed in terms of differential geometry and topology. In particular, we show that the mapping to S0 naturally encodes the binary outcomes of [...] Read more.
We present two propositions and a theorem to establish a foundational framework for a novel perspective on quantum information framed in terms of differential geometry and topology. In particular, we show that the mapping to S0 naturally encodes the binary outcomes of entangled quantum states, providing a minimal yet powerful abstraction of quantum duality. Building on this, we introduce the concept of a discrete fiber bundle to represent quantum steering and correlations, where each fiber corresponds to the two possible measurement outcomes of entangled qubits. This construction offers a new topological viewpoint on quantum information, distinct from traditional Hilbert-space or metric-based approaches. The present work serves as a preliminary formulation of this framework, with further developments to follow. Full article
(This article belongs to the Special Issue Advances in Quantum Information Processing: Theory, Methods and Emerging Applications)
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28 pages, 837 KB  
Article
A Comparative Study of Quantum Haar Wavelet and Quantum Fourier Transforms for Quantum Image Transmission
by Udara Jayasinghe, Thanuj Fernando and Anil Fernando
Information 2025, 16(11), 962; https://doi.org/10.3390/info16110962 - 6 Nov 2025
Viewed by 1359
Abstract
Quantum communication has achieved significant performance gains compared to classical systems but remains sensitive to channel noise and decoherence. These limitations become especially critical in quantum image transmission, where high-dimensional visual data must be preserved with both structural fidelity and robustness. In this [...] Read more.
Quantum communication has achieved significant performance gains compared to classical systems but remains sensitive to channel noise and decoherence. These limitations become especially critical in quantum image transmission, where high-dimensional visual data must be preserved with both structural fidelity and robustness. In this context, transform-based quantum encoding methods have emerged as promising approaches, yet their relative performance under noisy conditions has not been fully explored. This paper presents a comparative study of two such methods, the quantum Fourier transform (QFT) and the quantum Haar wavelet transform (QHWT), within an image transmission framework. The process begins with source coding (JPEG/HEIF), followed by channel coding to enhance error resilience. The bitstreams are then mapped into quantum states using variable qubit encoding and transformed using either QFT or QHWT prior to transmission over noisy quantum channels. At the receiver, the corresponding decoding operations are applied to reconstruct the images. Simulation results demonstrate that the QFT achieves superior performance under noisy conditions, consistently delivering higher Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Universal Quality Index (UQI) values across different qubit sizes and image formats compared to the QHWT. This advantage arises because QFT uniformly spreads information across all basis states, making it more resilient to noise. By contrast, QHWT generates localized coefficients that capture structural details effectively but become highly vulnerable when dominant coefficients are corrupted. Consequently, while QHWT emphasizes structural fidelity, QFT provides superior robustness, underscoring a fundamental trade-off in quantum image communication. Full article
(This article belongs to the Special Issue Advances in Quantum Information Processing: Theory, Methods and Emerging Applications)
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