Quantum generative and classification models applied to particle physics and medicine

Quantum and High Performance Computing (QHPC)

Group Description:

The Quantum and High-Performance Computing (QHPC) research group at the University of Oviedo has over twenty years of experience applying high-performance computing techniques to problems in machine learning, computational algebra, and numerical computation. For the past six years, it has also incorporated a research line in quantum computing, with applications in algebraic problems, high-energy physics, biomedical signal processing, and the development of quantum circuit simulation systems.

The QHPC group collaborates on quantum computing research projects with researchers from institutions such as CERN, the University of Cambridge, ETH Zürich, EPFL Lausanne, the University of Almería, the University of Jaén, the CTIC Foundation, and the Technological Institute of Castilla y León.

The group is composed of the following researchers: José Ranilla Pastor, Raquel Cortina Parajón, Pablo Revuelta Sanz, and Elías Fernández-Combarro Álvarez.

Activity description:

The tasks of the QHPC group focus on applying Quantum Machine Learning techniques to the processing of biomedical signals. Additionally, the group will collaborate on training and dissemination activities related to the objectives of the Quantum Spain project.

Specifically, the following tasks will be addressed:

  • Develop models based on quantum classifiers applied to healthcare problems, especially those related to the processing of biomedical signals, specifically those where the data source is of a sonic nature (lung sounds, heart sounds, etc.).
  • Carry out the creation of informative materials related to quantum computing and Quantum Machine Learning. This material will include educational content such as courses at different levels, specialized audiovisual material, or interactive code.


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