Special Issue "Quantum Machine Learning: Theory, Methods and Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 4019

Special Issue Editors

Dr. Weiwen Jiang
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA 22030, USA
Interests: Edge Computing; Quantum Computing
Department of Computer and Information Science, Fordham University, New York, NY 10458, USA
Interests: Quantum Machine Learning; Quantum Networks; Quantum Cloud
Computational Science Initiative, Brookhaven National Laboratory, New York, NY 11973-5000, USA
Interests: quantum computing; quantum machine learning; quantum optimal control; quantum error correction
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Special Issue Information

Dear Colleagues,

Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Famous examples are Shor’s algorithm, which can be used to factorize large numbers and can potentially threaten current state-of-the-art public-key cryptography systems, and Grover’s algorithm, which can provide a quadratic speedup in the unstructured database search.

Meanwhile, machine learning (ML) technologies are widely developed both in academia and industries. For example, ML technologies have been shown to be highly successful in computer vision, natural language processing and even mastering the game of Go with superhuman-level performance. Quantum machine learning (QML), a promising research direction, is to build an even more powerful AI/ML framework that can leverage the advantages provided by quantum physics. Notably, recent advances in quantum computing hardware shed light on the reality of such algorithms. Variational quantum circuits (VQC)-based quantum machine learning algorithms have been shown to be highly successful in various applications such as classification, function approximation, generative modeling, sequential modeling, federated learning and deep reinforcement learning. However, there are still several challenges to solve. For example, the optimization of VQC-based models can potentially suffer from the Barren plateaus, which hinder the scalability of QML models. On the other hand, works exist to map the existing neural networks to the quantum computers and demonstrate the potential quantum advantage that can be achieved for the quantum neuron via a co-design. However, it is still questionable whether such a design can be scaled up for deep learning. Therefore, it is highly non-trivial to design a proper QML architecture for specific commercial or scientific applications. Such difficulties make domain experts inaccessible to the power of quantum machine learning. 

This Special Issue will include most aspects of QML research, such as theoretical investigation, practical software implementation and real-world applications. All paradigms of QML are welcome. This Special Issue will bring both quantum computing and classical machine learning together and shed light on promising new research directions as well as valuable applications for either industries or academia.

Dr. Weiwen Jiang
Dr. Ying Mao
Dr. Samuel Yen-Chi Chen
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. Electronics is an international peer-reviewed open access semimonthly 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 2200 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.


  • Quantum machine learning
  • Quantum neural network
  • Quantum supervised learning
  • Quantum unsupervised learning
  • Quantum reinforcement learning
  • Quantum learning theory
  • Variational quantum circuits
  • Noisy intermediate-scale quantum devices (NISQ)

Published Papers (1 paper)

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20 pages, 1833 KiB  
Design Space Exploration of Hybrid Quantum–Classical Neural Networks
Electronics 2021, 10(23), 2980; https://doi.org/10.3390/electronics10232980 - 30 Nov 2021
Cited by 5 | Viewed by 2375
The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both [...] Read more.
The unprecedented success of classical neural networks and the recent advances in quantum computing have motivated the research community to explore the interplay between these two technologies, leading to the so-called quantum neural networks. In fact, universal quantum computers are anticipated to both speed up and improve the accuracy of neural networks. However, whether such quantum neural networks will result in a clear advantage on noisy intermediate-scale quantum (NISQ) devices is still not clear. In this paper, we propose a systematic methodology for designing quantum layer(s) in hybrid quantum–classical neural network (HQCNN) architectures. Following our proposed methodology, we develop different variants of hybrid neural networks and compare them with pure classical architectures of equivalent size. Finally, we empirically evaluate our proposed hybrid variants and show that the addition of quantum layers does provide a noticeable computational advantage. Full article
(This article belongs to the Special Issue Quantum Machine Learning: Theory, Methods and Applications)
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