Mario Motta, Gavin Jones, et al.
ACS Fall 2023
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.
Mario Motta, Gavin Jones, et al.
ACS Fall 2023
Sebastian Brandhofer, Ilia Polian, et al.
IEEE TQE
Monit Sharma, Yan Jin, et al.
QCE 2024
Mohammad Hassan, Fabijan Pavosevic, et al.
APS March Meeting 2023