Publications

This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. We find that quantum models tend to outperform classical approaches as the problem complexity increases, as QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies.

• Email
• |
• Work Package 4

A multi-task deep-learning approach, inspired by computer vision's object detection, is proposed to simultaneously detect sleep events and construct hypnograms, streamlining the complex process of polysomnographic sleep analysis.

• Publication
• Email
• |
• Work Package 2

We present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.

• Publication
• Email
• |
• Work Package 4

This "Data Management Plan" document aims to set the lifecycle management plan for handling research data that will be collected, generated, and/or processed within the context of the project "Next Generation Machine Learning Algorithms for the Analysis of Medical Sleep Recordings" (NEXT-GEN-SOMNUS) according to the "FAIR data" and "as open as possible, as closed as necessary" principles.

• Publication
• Email
• |
• Work Package 8