Quantum Pangenomics

Genomics is currently driving a huge improvement in our understanding of Human development and disease. Pangenomics is the next step on this journey, as understanding the variation in many genomes is key to unravelling how genetic traits can affect health outcomes. Unfortunately, building and analysing a pangenome is computationally intensive. Quantum computing may offer ways of overcoming this complexity, allowing more accurate pangenome representations to be constructed. This will enable more precise description of genetic variation, leading to improved diagnostics and treatments for a diverse range of medical conditions.

Genome sequencing is vital for applications used in monitoring disease outbreaks and personalised medicine. The structure of many challenging problems in computational genomics and pangenomics in particular makes them suitable candidates for speedups promised by quantum computing. The resulting advances have the potential to unlock transformative health benefits that depend on large-scale genomic analysis.

In Phase 1 of the project, our team adapted complex problems like genome assembly and construction of phylogenetic trees into a hybrid quantum-classical framework, enabling promising quantum speedups with emerging technology. Our key innovations include scalable quantum data encoding algorithms, setting the stage for storing and manipulating significant amounts of genomic data, and faster algorithms for genome assembly and phylogenetic tree inference. 

As we move into Phase 2, we look forward to simulating our new approaches with HPC using machine-learning-oriented encoding schemes and tensor network methods. Alongside this work, we will test the ability of our algorithms to resolve parts of the genome graph that are intractable classically. We will gain further insight into performance at scale, enabling us to move forward in Phase 3 to implementation on real quantum hardware. In collaboration with quantum hardware vendors, we will ensure that our proposed implementations account for hardware-specific architecture and noise properties.