Advances in Quantum Machine Learning

Dear Colleagues,

Our upcoming Special Issue aims to delve into the realm of quantum machine learning, a rapidly evolving field at the intersection of quantum computing and machine learning. This collection will serve as a platform to explore the latest advancements, methodologies, and applications in quantum-enhanced machine learning algorithms and techniques.

The primary focus of this Special Issue is to gather cutting-edge research and insights on quantum machine learning, spanning topics such as quantum algorithms for machine learning tasks, quantum data encoding and processing, and hybrid classical–quantum approaches. We aim to highlight groundbreaking studies that harness the power of quantum computing to address complex machine learning challenges and unlock new capabilities.

The scope of this collection encompasses a broad range of topics within quantum machine learning, including but not limited to the following:

• Quantum algorithms for classification, regression, clustering, and optimization;
• Quantum neural networks and quantum-enhanced deep learning architectures;
• Quantum-enhanced feature selection and dimensionality reduction;
• Quantum data encoding, representation, and processing techniques;
• Quantum-inspired AI algorithms and their applications across various domains, including energy, finance, cybersecurity, 6G, healthcare, chemistry, and beyond;
• Quantum robotics and control systems;
• Quantum annealing and high-performance computing (HPC) for AI;
• AI for quantum compilers, error correction, and mitigation;
• AI for quantum gate synthesis, algorithms, and circuit optimization and design;
• AI for quantum circuit mapping and hardware design;
• AI for quantum resource allocation.

By embracing this comprehensive scope, we aim to capture the diversity of research efforts in quantum machine learning and provide insights into both theoretical advancements and practical implementations. This Special Issue aims to push the boundaries of quantum machine learning and pave the way for future advancements in this exciting field.

We eagerly anticipate your contributions to this Special Issue and the valuable insights and discussions it will generate.

Dr. Xing Liang
Prof. Dr. Nila Nilavalan
Prof. Dr. Hongying Meng
Prof. Dr. Hongwei Wu
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 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 machine learning
• quantum neural networks
• hybrid classical–quantum approaches
• quantum annealing
• quantum-enhanced AI applications