2020/07/14 by Daniel Sierra-Sosa, Juan D. Arcila-Moreno, Sierra-Sosa, Daniel +7 · 2 citations
Computer Science · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2007.08653
openalex publication_date 2020/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dementia is the fifth cause of death worldwide with 10 million new cases every year. Healthcare applications using machine learning techniques have almost reached the physical limits while more data is becoming available resulting from the increasing rate of diagnosis. Recent research in Quantum Machine Learning (QML) techniques have found different approaches that may be useful to accelerate the training process of existing machine learning models and provide an alternative to learn more complex patterns. This work aims to report a real-world application of a Quantum Machine Learning Algorithm, in particular, we found that using the implemented version for Variational Quantum Classiffication (VQC) in IBM's framework Qiskit allows predicting dementia in elderly patients, this approach proves to provide more consistent results when compared with a classical Support Vector Machine (SVM) with a linear kernel using different number of features.