Abstract

Genomics is a transformational technology for biology, driving a massive improvement in our understanding of human biology and disease. Pangenomics is an important next step on this journey, as understanding variation across many genomes is key to unravelling how genetic traits can affect health outcomes. Building and analysing a pangenome is computationally intensive. Many essential tasks in genomic analysis are extremely difficult for classical computers due to problems inherently hard to solve efficiently with classical (empirical) algorithms. Quantum computing offers novel possibilities with algorithmic techniques capable of achieving speedups over existing classical exact algorithms in large-scale genomic analyses.   Funded by the Wellcome Leap Q4Bio program (https://wellcomeleap.org/q4bio/), we pursue two main research thrusts.1. Algorithm Development: We will design novel quantum algorithms for multiple sequence alignment subproblems and investigate heuristic methods (QAOA) for de novo assembly. 2. Data Encoding and State Preparation: We aim to develop efficient quantum circuits to encode genomic data and reduce the computational overhead with a variety of techniques, including tensor network representations. It facilitates data encoding into quantum states for a variety of machine-learning applications.