Quantum Computing and Artificial Intelligence Inclusive and behind Quantum Machine Learning

Dear Colleagues,

Quantum computers are on the way to becoming large-scale and fault-tolerant, hence providing real value in comparison to traditional computing architectures. Quantum machine learning is the quantum counterpart of machine learning on classical hardware. While many contributions claim superior performance of quantum in comparison to classical machine learning, there has been also some doubts about the overall benefits of quantum machine learning. This Special Issue is open for positive reports on quantum machine learning as well as on those dealing with a reflective discussion on quantum machine learning with the purpose of shedding light on the real values of quantum machine learning. Furthermore, we encourage authors to submit papers on quantum artificial intelligence besides machine learning like expert systems, symbolic artificial intelligence, knowledge representation and reasoning, genetic algorithms and evolutionary computation, fuzzy logic systems and constraint satisfaction problems, where quantum computing helps to improve the performance or the results. Furthermore, we consider contributions reporting on artificial intelligence approaches to improve quantum computing technologies and applications.

This Special Issue aims to explore recent advancements and challenges in quantum computing and artificial intelligence, focusing on quantum machine learning and approaches not falling into the traditional categories of quantum machine learning.

Topics of Interest:

• Quantum Machine Learning; o Quantum Generative AI;
• Time-Series Data ;
• Quantum Anomaly Detection;
• Quantum Supervised and Unsupervised Learning;
• Data Encoding Approaches for Quantum Machine Learning ;
• Comparison and critical discussion of Machine Learning versus Quantum Machine Learning.
• Quantum Artificial Intelligence besides Quantum Machine Learning in the areas of:
• Expert systems;
• Symbolic artificial intelligence;
• Knowledge representation and reasoning;
• Genetic algorithms and evolutionary computation;
• Fuzzy logic systems;
• Constraint satisfaction problems.
• Artificial Intelligence Approaches for Quantum Computing for improving quantum computing technologies and applications.

Prof. Dr. Sven Groppe
Dr. Jinghua Groppe
Guest Editors

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• quantum machine learning
• artificial intelligence
• knowledge representation and reasoning
• genetic algorithms and evolutionary computation