Quantum Machine Learning and Computation: Methods, Architectures and Applications

Dear Colleagues,

Quantum machine learning (QML) is rapidly transforming intelligent systems by enabling learning and decision‑making capabilities that operate effectively under uncertainty, high dimensionality and dynamic conditions. Hybrid quantum–classical architectures, variational quantum circuits and quantum feature encoders are beginning to support street‑smart computational behaviors—systems that can learn, reason and adapt when information is incomplete, environments are unstructured and assumptions break down. These advances are accelerating progress across engineering domains such as signal processing, wireless communications, cybersecurity, robotics and complex system optimization.

This Special Issue, Quantum Machine Learning and Computation: Methods, Architectures and Applications, aims to gather recent breakthroughs that advance the theory, design and deployment of QML systems. We welcome contributions on quantum circuit architectures, variational algorithms, quantum encoders, optimization strategies, error‑mitigation techniques and resource‑efficient computation for NISQ devices. Application‑driven studies—such as quantum‑assisted communication, quantum‑enhanced sensing, intelligent control and secure distributed systems—are strongly encouraged.

By integrating methodological innovations, architectural advances and emerging applications, this Special Issue seeks to highlight how quantum machine learning can enable the next generation of street‑smart intelligent systems. Our goal is to provide a comprehensive and timely overview of current progress in quantum machine learning and computational quantum technologies.

Dr. Tan Le
Dr. Sachin Shetty
Guest Editors

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