Genetic Merit Estimation for Plant and Animal Breeding

Estimating the genetic merits (an individual's estimated genetic potential for specific traits) of millions of livestock animals involves solving large-scale linear equation systems (with billions of equations). Complexity continually increases with the addition of multi-dimensional data.

Quantum algorithms, such as the Harrow–Hassidim–Lloyd (HHL) algorithm, offer potential speed-ups when solving large, sparse linear equation systems. Hybrid approaches, like the variational quantum linear solver, may also offer speed-ups.

This allows for faster and more efficient estimation of genetic merits. It could enable the integration of increasing data dimensions (e.g., transcriptomics or methylation data) into genomic prediction models.