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AI
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Advances in Quantum Computing and Quantum Machine Learning
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Advances in Quantum Computing and Quantum Machine Learning

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5712

Special Issue Editor

Dr. Arslan Munir

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Interests: artificial intelligence; computer vision; parallel computing; embedded systems; secure and trustworthy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantum computing has emerged as a new field of scientific research and development that exploits the synergy between quantum mechanisms and computer science. Quantum computing has stirred a burgeoning revolution in the fields of computing and machine learning. Although quantum computers have some of their components named similar to those of classical computers, such as registers, gates, and memory elements, their underlying physical structures are fundamentally distinct and unique. Quantum computers operate on qubits (the quantum counterpart of classical bits) that are capable of existing in states of zero, one, or any intermediate value and exhibit superposition and entanglement properties. These unique attributes empower quantum computers to simultaneously follow multiple computational paths within a single calculation, which is not possible by classical computers without repeated iterations. Quantum computing is capable of enhancing the machine learning design process due to its ability to speed up linear algebraic operations exponentially as state space grows. With the advent of noisy intermediate-scale quantum (NISQ) processors, quantum machine learning based on heuristic methods has gained momentum due to the increased computational capabilities of quantum hardware, particularly in the field of deep learning. Since quantum processors are still fairly small and noisy, to improve machine learning performance effectively, NISQ processors often work with classical co-processors in hybrid mode, giving rise to hybrid quantum–classical machine learning.

This Special Issue targets advances in quantum computing and quantum machine learning. This Special Issue invites original research articles and reviews that relate to the circuits, algorithms, implementation, and applications of quantum computing and quantum machine learning. All fields of quantum computing and machine learning, including hybrid quantum–classical computing and machine learning, are of interest to this Special Issue. Topics of interest include, but are not limited to, the following:

  • Quantum computing;
  • Quantum machine learning;
  • Hybrid quantum–classical computing;
  • Hybrid quantum–classical machine learning;
  • Noisy intermediate-scale quantum (NISQ) processing;
  • Circuits for quantum computing and machine learning;
  • Performance analysis of classical versus quantum computing;
  • Hybrid quantum–classical deep learning;
  • Hybrid quantum–classical neural networks;
  • Applications of (hybrid) quantum computing;
  • Applications of (hybrid) quantum machine learning.

Dr. Arslan Munir
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 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. AI 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 1600 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 computing
  • quantum machine learning
  • hybrid quantum-classical computing
  • hybrid quantum-classical machine learning
  • noisy intermediate-scale quantum processing
  • quantum algorithms
  • quantum circuits

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Published Papers (2 papers)

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28 pages, 1119 KiB  
Article
HNN-QCn: Hybrid Neural Network with Multiple Backbones and Quantum Transformation as Data Augmentation Technique
by Yuri Gordienko, Yevhenii Trochun, Vladyslav Taran, Arsenii Khmelnytskyi and Sergii Stirenko
AI 2025, 6(2), 36; https://doi.org/10.3390/ai6020036 - 13 Feb 2025
Viewed by 649
Abstract
Purpose: The impact of hybrid quantum-classical neural network (NN) architectures with multiple backbones and quantum transformation as a data augmentation (DA) technique on image classification tasks was investigated using the CIFAR-10 and MedMNIST (DermaMNIST) datasets. These datasets were chosen for their relevance in [...] Read more.
Purpose: The impact of hybrid quantum-classical neural network (NN) architectures with multiple backbones and quantum transformation as a data augmentation (DA) technique on image classification tasks was investigated using the CIFAR-10 and MedMNIST (DermaMNIST) datasets. These datasets were chosen for their relevance in general-purpose and medical-specific small-scale image classification, respectively. Methods: A series of quanvolutional transformations, utilizing random quantum circuits based on single-qubit rotation quantum gates (Y-axis, X-axis, and combined XY-axis transformations), were applied to create multiple quantum channels (QC) for input augmentation. By integrating these QCs with baseline convolutional NN architectures (LCNet050) and scalable hybrid NN architectures with multiple (n) backbones and separate QC (n) inputs (HNN-QCn), the scalability and performance enhancements offered by quantum-inspired data augmentation were evaluated. The proposed cross-validation workflow ensured reproducibility and systematic performance evaluation of hybrid models by mean and standard deviation values of metrics (such as accuracy and area under the curve (AUC) for the receiver operating characteristic). Results: The results demonstrated consistent performance improvements by AUC and accuracy in HNN-QCn models with the number n (where n∈{4,5,9,10,17,18}) of backbones and QC inputs across both datasets. The different improvement rates were observed for the smaller increase in AUC and the larger increase in accuracy as input complexity (number of backbones and QCs inputs) increases. It is assumed that the prediction probability distribution is becoming sharpened with the addition of backbones and QC inputs, leading to larger improvements in accuracy. At the same time, AUC reflects these changes more slowly unless the model’s ranking ability improves substantially. Conclusion: The findings highlight the scalability, robustness, and adaptability of HNN-QCn architectures, with superior performance by AUC (micro and macro) and accuracy across diverse datasets and potential for applications in high-stakes domains like medical imaging. These results underscore the utility of quantum transformations as a form of DA, paving the way for further exploration into the scalability and efficiency of hybrid architectures in complex datasets and real-world scenarios. Full article
(This article belongs to the Special Issue Advances in Quantum Computing and Quantum Machine Learning)
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20 pages, 5693 KiB  
Article
H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification
by Muhammad Asfand Hafeez, Arslan Munir and Hayat Ullah
AI 2024, 5(3), 1462-1481; https://doi.org/10.3390/ai5030070 - 19 Aug 2024
Cited by 2 | Viewed by 4289
Abstract
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image [...] Read more.
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications. Full article
(This article belongs to the Special Issue Advances in Quantum Computing and Quantum Machine Learning)
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