Project Lead: Quantum Genomics
Sector: Healthcare
Project Summary
The QResolve project addresses a major bottleneck in genomic sequencing: resolving complex genomic assembly graphs that arise during variant calling in difficult regions of the genome. These graph-resolution problems can be computationally expensive and may limit both accuracy and throughput in regulatory genomics workflows.
The project will reformulate these tasks as Quadratic Unconstrained Binary Optimisation (QUBO) and Higher-Order Unconstrained Binary Optimisation (HUBO) problems. Solutions will first be benchmarked using exact and advanced classical optimisation methods, then evaluated using the Iterative Quantum Approximate Optimisation Algorithm (Iterative QAOA) on quantum simulators and quantum hardware. The approach builds on previous experiments conducted on IBM quantum processors (24-48 qubits) and will use available gate-based quantum hardware.
Expected outcomes include validated optimisation methods, a reusable genomic graph benchmark suite, quantum and classical performance comparisons, a Sarek-compatible prototype module, biological validation against Genome in a Bottle datasets, and a decision framework identifying when classical, annealing, or gate-based quantum approaches are most effective.
