Quantum Pangenomics

Using a quantum optimisation approach in a hybrid classical-quantum method to tackle computationally intensive problems in genomics and pangenomics, including tangle resolution, phylogenetic tree construction, and efficient genomic data encoding using MPS- and ML-based approaches.

`Owner`

`Quantum Approach`

Optimisation

`SDGs`

`Contributors`

`Origins of Contributors`

University of Cambridge

University of Oxford

Wellcome Sanger Institute

European Bioinformatics Institute | EMBL-EBI

Kyiv Academic University

`How quantum could help`

Complex problems such as genome assembly and the construction of phylogenetic trees could be optimised in a hybrid quantum-classical framework, enabling promising quantum speedups with emerging technology. These speedups could be further achieved by including scalable quantum data encoding algorithms that support the storage and manipulation of significant amounts of genomic data, along with faster algorithms for genome assembly, phylogenetic tree inference, and in tangle resolution and phylogenetic tree construction. One of the main aims would be to test the algorithms’ performance and ability to resolve parts of the genome graph that are classically intractable. In doing so, a deeper insight into performance at scale would be gained, where resulting advances have the potential to unlock transformative health benefits that depend on large-scale genomic analysis.