Submission Deadline: 01 October 2025 View: 150 Submit to Special Issue
Prof. Dr. Jong Hyuk Park
Email: jamespark1.research@gmail.com
Affiliation: Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Korea
Research Interests: quantum machine learning, AI, quantum security
Dr. Abir EL Azzaoui
Email: abir.el@seoultech.ac.kr
Affiliation: Department of Computer Science and Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811, Korea
Research Interests: quantum information science, quantum machine learning
1. Introduction
Quantum Machine Learning (QML) and Deep Learning are at the forefront of technological innovation, merging the principles of quantum computing with advanced neural network architectures to revolutionize computational systems. QML leverages quantum computing's unique properties, such as superposition and entanglement, to process information in ways unattainable by classical computers, offering exponential speed-ups for specific tasks. This convergence is poised to transform various sectors, including artificial intelligence, cryptography, and complex system modeling, by enabling more efficient data processing and problem-solving capabilities.
2. Aim and Scope
This Special Issue aims to gather pioneering research that explores the integration of Quantum Machine Learning and Deep Learning within Future Generation Computing Systems. We seek contributions that address theoretical advancements, practical implementations, and novel applications of QML and Deep Learning, highlighting their potential to redefine computational paradigms. The scope encompasses interdisciplinary studies that bridge quantum computing, machine learning, and system architecture to foster the development of next-generation computing solutions.
3. Suggested Themes
- Quantum Algorithms for Machine Learning: Development and analysis of quantum algorithms that enhance machine learning processes, including quantum versions of classical learning models.
- Quantum Neural Networks: Design and implementation of neural networks operating on quantum principles, such as Quantum Convolutional Neural Networks (QCNNs), and their applications in data classification and pattern recognition.
- Hybrid Quantum-Classical Systems: Exploration of systems that integrate quantum and classical computing resources to optimize performance and resource utilization in machine learning tasks.
- Quantum Computing Hardware for AI: Advancements in quantum hardware development, including qubit technologies and error correction mechanisms, tailored to support AI applications.
- Applications in Various Domains: Innovative applications of QML and Deep Learning in fields such as drug discovery, financial modeling, climate modeling, and secure communications.
- Benchmarking and Performance Evaluation: Studies assessing the performance of quantum-enhanced machine learning models compared to classical counterparts, including discussions on scalability and practicality.
- Software Frameworks and Tools: Development of software platforms and tools that facilitate the implementation and testing of QML and Deep Learning models on quantum hardware.
- Ethical and Societal Implications: Examination of the ethical considerations and potential societal impacts arising from the deployment of quantum-enhanced AI systems.
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