Quantum-inspired genomic selection
Problem
Genomic selection improves future generations by breeding animals or plants with desired traits. This process, which uses DNA markers and characteristics, promises higher yields, better quality, disease resistance, and sustainability. Together with researchers from Wageningen Livestock & Research (WLR), we are developing algorithms to tackle the computational challenges of large and complex datasets.
Solution
Researchers from QAL and WLR have developed randomised algorithms for singular value decompositions (SVD). These methods reduce the dimensionality of datasets, speed up calculations, and maintain accuracy. Quantum-inspired selection techniques focus on the top 5% with the highest genetic value, reducing inefficiency. Tools such as truncated SVD and Halko's algorithms significantly shorten computation times.
Benefit
This approach optimises genomic predictions, shortens the identification time of valuable individuals, and improves breeding programmes. This contributes to food security, biodiversity conservation, and more sustainable agriculture.
This work is supported by the Dutch National Growth Fund (NGF) as part of the Quantum Delta NL programme.
