Submission Deadline: 31 March 2023 (closed)
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.