Genomic Variant Detection and Prioritisation

After sequence alignment, identifying genetic variants (SNPs, structural variants) and prioritising those linked to observable characteristics or traits (phenotypes), is crucial for understanding genetic contributions to health and disease. This requires large-scale statistical analyses (GWAS, QTL) and complex optimisations, which strain current computational methods.

Quantum optimisation (e.g. Quantum Least Squares, Quantum Boltzmann Machines) and quantum-enhanced Bayesian inference can accelerate regression, improve model fitting, and better capture complex genotype-phenotype relationships. Quantum machine learning methods, such as quantum neural networks and variational classifiers, are also seen as potential tools to address these computational challenges.

Knowing which genetic variants are associated with specific conditions or biological characteristics gives insights into disease etiology and potentially pave the way for new diagnostic or therapeutic approaches.