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Applications of Quantum Machine Learning in Biomedical Domain

Submission Deadline: 31 March 2023 (closed)

Guest Editors

Dr. Muhammad Zubair Asghar, Gomal University, Pakistan; University of Kuala Lumpur (UniKL), Malaysia.
Dr. Shakeel Ahmad, King Abdulaziz University, Saudi Arabia.
Prof. Ibrahim A Hameed, Norwegian University of Science and Technology, Norway.


Due to ever-increasing computing power and advances in machine learning algorithms, quantum technologies have emerged as potent tools for various application fields, including chemistry, agriculture, natural language processing, and health care. Another growing subject is quantum machine learning, which processes classical data and machine learning algorithms in the quantum realm. Quantum machine learning has become a widespread and successful technology for data processing and classification in many fields. This is the most prevalent usage of quantum computing: quantum machine learning (QML).
Traditional machine learning, may be able to learn from massive amounts of data, but it cannot deal with more complicated structures or better handle noisy input. There will be more advantages in the near future, though. Quantum machine learning may be able to learn from a less amount of data, handle more complicated structures, or handle noisy input better than traditional machine learning algorithms.
This special issue focuses on biomedical applications, which supports a varied range of research topics encompassing several applications, such as medical specializations and diseases associated with those specializations. While some of these diseases are well-known and understood by medical professionals, others do not fall into those categories. The clinical evaluations and metrics, biological factors, and medical imaging modalities available to medical practitioners due to recent scientific and technological breakthroughs have grown increasingly varied. Due to the sheer volume of data and the exhaustiveness of some abnormal states, biomedical data are typically asymmetrical, non-stationary, and categorized by a high degree of complexity. This is because of the nature of the data itself.


This special issue proposes applicability of Quantum Machine Learning in Biomedical Domain, but not limited to the following:
• Using QML to analyze, classify, predict, and diagnose medical images.
• the use of several QML algorithm types in healthcare such as Quantum Support Vector Machine, Quantum Inspired ML, Variational Quantum Classifier, Quantum Neural Network, and Quantum Random Access Coding, as well as the Quantum Nearest Mean Classifier and the Hybrid Quantum Feature Selection Algorithm, were tested on publicly available UCI ML healthcare datasets.
• Classical ML and QML in healthcare.
• QML's use in Omics, biomedical imaging, biosignals, and medical records.
• Using QML with publicly accessible healthcare data.
• Utilizing quantum computing devices to analyse healthcare data.

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