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Quantum Reports
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Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research
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Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research

A special issue of Quantum Reports (ISSN 2624-960X).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5237

Special Issue Editors

Dr. Fahad Ahmad

E-Mail Website
Guest Editor
1. School of Computing, Faculty of Technology, University of Portsmouth, Winston Churchill Ave., Southsea, Portsmouth PO1 2UP, UK
2. Portsmouth Artificial Intelligence and Data Science Centre, University of Portsmouth, Winston Churchill Ave., Southsea, Portsmouth PO1 2UP, UK
Interests: quantum computing; machine learning; deep learning; quantum machine learning applications
Prof. Dr. Vincenzo Tamma

E-Mail Website
Guest Editor
School of Mathematics and Physics, University of Portsmouth, Portsmouth PO1 3HF, UK
Interests: quantum computation; quantum communication; simulation of complex quantum systems; high-precision sensing and imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Quantum Machine Learning (QML), the fusion of quantum computing and machine learning, offers unprecedented opportunities to advance research across multiple scientific fields. Quantum-enhanced algorithms provide powerful tools for overcoming the limitations of classical methods, enabling researchers to tackle complex problems with greater accuracy, efficiency, and speed.

Recent breakthroughs in quantum simulators, variational quantum algorithms, and quantum generative models have made it possible to apply quantum computing to a range of challenging tasks, such as predictive modelling, optimization, and large-scale data analysis. As access to quantum hardware expands, the potential for QML to revolutionize diverse sectors, including but not limited to materials science, environmental monitoring, healthcare, finance, and aerospace, is increasingly becoming tangible.

This Special Issue invites original contributions, technical papers, simulations, and reviews that explore the theoretical foundations, practical applications, and forward-looking perspectives of QML and quantum generative models across various interdisciplinary research areas. While the primary focus will be on the applications of QML in these fields, the scope of this Special Issue is not limited to the defined fields and welcomes contributions from all areas where QML can have a transformative impact.

We aim to highlight how quantum computing can transcend classical limitations and accelerate breakthroughs across a wide range of scientific domains, fostering innovation and expanding the boundaries of what is possible in research and technology.

Dr. Fahad Ahmad
Prof. Dr. Vincenzo Tamma
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. Quantum Reports is an international peer-reviewed open access quarterly 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 1400 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 generative models
  • quantum neural networks
  • hybrid quantum–classical algorithms
  • quantum optimization
  • predictive modelling and optimization using QML
  • QML in materials science and energy storage
  • quantum computing in environmental monitoring
  • quantum algorithms for healthcare and medical diagnostics
  • financial modelling and risk analysis with QML
  • quantum-enhanced applications in aerospace and engineering

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

Published Papers (4 papers)

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35 pages, 2019 KB  
Article
Defining Quantum Agents: Formal Foundations, Architectures, and NISQ-Era Prototypes
by Eldar Sultanow, Madjid Tehrani, Siddhant Dutta, William J. Buchanan and Muhammad Shahbaz Khan
Quantum Rep. 2026, 8(1), 24; https://doi.org/10.3390/quantum8010024 - 13 Mar 2026
Viewed by 331
Abstract
Quantum computing offers potential computational advantages, yet its integration into autonomous decision-making systems remains largely unexplored. This paper addresses the need for a unified framework that systematically combines quantum computation with agent-based artificial intelligence. We examine how quantum technologies can enhance the capabilities [...] Read more.
Quantum computing offers potential computational advantages, yet its integration into autonomous decision-making systems remains largely unexplored. This paper addresses the need for a unified framework that systematically combines quantum computation with agent-based artificial intelligence. We examine how quantum technologies can enhance the capabilities of autonomous agents and, conversely, how agentic AI can support the advancement of quantum systems. We analyze both directions of this synergy and present conceptual and technical foundations for future quantum–agentic platforms. Our work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, we develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. Furthermore, we discuss application domains including quantum-enhanced optimization, hybrid quantum–classical orchestration, autonomous quantum workflow management, and secure quantum information processing. By bridging these fields, we introduce a structured theoretical and architectural framework for quantum–agentic systems, providing formal definitions, system models, and early operational prototypes that illustrate the feasibility of quantum-enhanced agency under NISQ constraints. Full article
(This article belongs to the Special Issue Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research)
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11 pages, 899 KB  
Article
Quantum-Inspired Classical Convolutional Neural Network for Automated Bone Cancer Detection from X-Ray Images
by Naveen Joy, Sonet Daniel Thomas, Aparna Rajan, Lijin Varghese, Aswathi Balakrishnan, Amritha Thaikkad, Vidya Niranjan, Abhithaj Jayanandan and Rajesh Raju
Quantum Rep. 2026, 8(1), 19; https://doi.org/10.3390/quantum8010019 - 25 Feb 2026
Viewed by 400
Abstract
Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in [...] Read more.
Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions. Full article
(This article belongs to the Special Issue Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research)
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23 pages, 2192 KB  
Article
Translating the Nearest Convex Hull Classifier from Classical to Quantum Computing
by Grégoire Cattan, Anton Andreev and Quentin Barthélemy
Quantum Rep. 2025, 7(4), 51; https://doi.org/10.3390/quantum7040051 - 28 Oct 2025
Viewed by 864
Abstract
The nearest convex hull (NCH) classifier is a promising algorithm for the classification of biosignals, such as electroencephalography (EEG) signals, especially when adapted to the classification of symmetric positive definite matrices. In this paper, we implemented a version of this classifier that can [...] Read more.
The nearest convex hull (NCH) classifier is a promising algorithm for the classification of biosignals, such as electroencephalography (EEG) signals, especially when adapted to the classification of symmetric positive definite matrices. In this paper, we implemented a version of this classifier that can execute either on a traditional computer or a quantum simulator, and we tested it against state-of-the-art classifiers for EEG classification. This article addresses the practical challenges of adapting a classical algorithm to one that can be executed on a quantum computer or a quantum simulator. One of these challenges is to find a formulation of the classification problem that is quadratic, is binary, and accepts only linear constraints—that is, an objective function that can be solved using a variational quantum algorithm. In this article, we present two approaches to solve this problem, both compatible with continuous variables. Finally, we evaluated, for the first time, the performance of the NCH classifier on real EEG data using both quantum and classical optimization methods. We selected a particularly challenging dataset, where classical optimization typically performs poorly, and demonstrated that the nearest convex hull classifier was able to generalize with a modest performance. One lesson from this case study is that, by separating the objective function from the solver, it becomes possible to allow an existing classical algorithm to run on a quantum computer, as long as an appropriate objective function—quadratic and binary—can be found. Full article
(This article belongs to the Special Issue Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research)
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Other

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24 pages, 649 KB  
Perspective
Quantum-Enhanced Algorithmic Fairness and the Advancement of AI Integrity and Responsibility
by Akhil Chintalapati, Khashbat Enkhbat, Ramanathan Annamalai, Geraldine Bessie Amali, Fatih Ozaydin and Mathew Mithra Noel
Quantum Rep. 2025, 7(3), 36; https://doi.org/10.3390/quantum7030036 - 11 Aug 2025
Cited by 1 | Viewed by 2690
Abstract
In the evolving digital landscape, the pervasive influence of artificial intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, [...] Read more.
In the evolving digital landscape, the pervasive influence of artificial intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the present work ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the quantum sentinel is presented—an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment. We also show that the quantum Zeno effect can protect SVM hyperplanes from bias through targeted simulations. Full article
(This article belongs to the Special Issue Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research)
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