Recent Advances in Quantum Machine Learning Technology

Dear Colleagues,

Recent advancements in quantum technology have sparked significant interest in the application of quantum computing principles to machine learning. This has given rise to the field of quantum machine learning (QML), which involves learning from classical data (e.g., images) or quantum data (e.g., from quantum sensing) using classical, quantum or even hybrid quantum–classical processors. A central research goal in this field is to investigate if and when quantum computing principles can offer advantages over classical computing, such as faster computation or enhanced model expressivity.

In recent years, this question has increasingly been investigated in the following two promising paradigms: quantum discriminative learning, which leverages quantum kernels and variational quantum models, and quantum generative learning. While recent research in both paradigms shows promising results, they face significant limitations due to the currently available noisy, intermediate scale quantum (NISQ) devices. This includes scalability of quantum models, which has led to the use of hybrid quantum–classical models, the presence of barren plateaus that impede the trainability of quantum models, and the detrimental effects of quantum gate noise on the model performance. Additionally, recent works have revealed the adversarial vulnerability of quantum classifiers.

This Special Issue will focus on recent research aimed at advancing QML on NISQ devices through (a) identifying novel applications of QML with demonstrable advantages, (b) addressing NISQ-related challenges for QML with novel approaches to mitigate them, and (c) enhancing the theoretical understanding of quantum machine learning. We invite new, unpublished research in areas including, but not limited to, the following:

• Real-world applications of quantum generative and discriminative learning particularly in genomics, health data or quantum chemistry;
• Quantum kernel methods and variational quantum models for scalable QML;
• Impact of quantum gate noise, quantum error mitigation and quantum error correction for machine learning;
• Theory of barren plateaus for the trainability of quantum models;
• Theoretical understanding of expressivity and generalization capacity of discriminative and generative learning models;
• Adversarial robustness of quantum models, including understanding vulnerability, novel defense mechanisms, and theoretical robustness guarantees.

Dr. Sharu Jose
Prof. Dr. Nikos Konofaos
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. Applied Sciences 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 2400 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 generative learning
• quantum kernels
• barren plateaus
• adversarial robustness
• quantum error mitigation