Recent Advances in Quantum Machine Learning and Applications in Physics and Mathematics

Dear Colleagues,

Machine learning has emerged as a leading tool for offering intelligent solutions to a diverse range of complex problems. Simultaneously, quantum computing has surfaced as a promising future technology, capable of solving specific problems much faster than classical computers, with the potential to revolutionize the computing landscape. Quantum computers are poised to deliver superior results and significantly boost the performance of machine learning tasks. The advancements in both machine learning and quantum computing have sparked growing interest in the integration of these two fields.

The scope of our Special Issue mainly focuses on quantum computing, machine learning, and other fields that are closely related to the mathematical aspects. It focuses on the following areas in mathematical methods and mathematical simulation:

• Quantum computing: theoretical and practical deployments in quantum computing;
• Quantum simulation: simulating techniques in quantum systems;
• Mathematical methods: these are effective tools for understanding the basic framework of the quantum system;
• Numerical simulation: computational method in deep research on quantum systems;
• Quantum machine learning: the integration of both principles and techniques for machine learning techniques;
• Open quantum systems: open boundaries for mathematical analysing systems.

Topics of interest also include, but are not limited to, efficient qubit-based data encoding for machine learning tasks, the optimization of parameterized quantum circuits, quantum-inspired algorithms, and quantum circuit optimization. Additionally, contributions exploring new methodologies, experimental studies, and practical implementations of quantum machine learning algorithms are highly encouraged. This Special Issue seeks to provide a comprehensive overview of the cutting-edge research at the intersection of quantum computing and machine learning from all areas of physics and mathematics.

Dr. Sergei Gleyzer
Dr. Kyoungchul Kong
Guest Editors

Manuscript Submission Information

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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 algorithms
• quantum computing
• quantum neural networks
• parameterized quantum circuits
• variational quantum algorithms
• particle physics
• high-energy physics
• mathematical physics
• mathematical analysis
• quantum simulation
• mathematical methods
• numerical simulation
• open quantum systems