Recent Applications of Machine Learning in Quantum Networks

Dear Colleagues,

The convergence of machine learning and the computational capabilities of quantum computing is attracting increasing interest as a novel methodology that leverages principles borrowed from quantum mechanics to provide a robust speedup to current AI approaches, reduce the amount of data necessary for training, overcome the computational constraints of current AI approaches and open new possibilities both for generative AI and general optimization tasks. Quantum machine learning (QML) has shown enormous potential, raising the performance bar in algorithm optimization and computational cost in a wide variety of tasks. QML approaches range from Quantum Support Vector Machines (QVMs), Quantum Variational Circuits (QVC), and Quantum Neural Networks (QNNs). In addition, a new paradigm is being created, given the growing interest in exploring the potential of QML in language-related tasks: Quantum Natural Language Processing (QNLP). This is a sub-field of research combining state-of-the-art NLP technologies, such as large language models (LLMs) or transformers, with the computational potential offered by quantum technologies.

Such approaches and recent advances in quantum machine learning, both from a theoretical and application perspective, are the focus of this Special Issue. It will provide up-to-date findings in theories, approaches, and experiments for a broad range of readers.

Topics of interest in this Special Issue include, but are not limited to, the following:

• Quantum algorithms;
• Quantum computing;
• Quantum communication;
• Quantum neural networks;
• Quantum language models:
• Quantum information processing;
• Quantum machine learning;
• Quantum transfer learning;
• Quantum natural language processing;
• Quantum information retrieval;
• Quantum text classification;
• Quantum hybrid approaches

Dr. Raffaele Guarasci
Dr. Giuseppe Buonaiuto
Dr. Arianna Maria Pavone
Dr. Giovanni Pilato
Guest Editors

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