Biomarker Discovery

Biomarkers are important indicators used in medicine for personalised diagnostics and interventions. However, finding biomarkers, particularly those that provide very early indicators for complex diseases, is challenging. This is because classical methods often struggle with detecting intricate correlations and effectively handling complex data types, such as small datasets with few samples, those with very high dimensionality, or data that is noisy or incomplete.

Quantum algorithms are being explored for various learning tasks relevant to biomarker discovery. Quantum computing, especially quantum machine learning, can be used for prediction, clustering, and handling errors.

The impact lies in potentially enhancing precision diagnostics and enabling earlier disease detection.