Arian Vezvae, Khadijeh Najafi, et al.
APS March Meeting 2024
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, particularly for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types --- multi-dimensional, time series, and erroneous data --- and covers key data modalities in healthcare --- electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.
Arian Vezvae, Khadijeh Najafi, et al.
APS March Meeting 2024
Marco Antonio Guimaraes Auad Barroca, Rodrigo Neumann Barros Ferreira, et al.
Paraty Quantum Information School and Workshop 2023
Gian Gentinetta, David Sutter, et al.
QCE 2023
Mirko Amico, David McKay, et al.
APS March Meeting 2023