Researchers are applying quantum computing to a longstanding challenge in genomic sequencing: resolving complex assembly graphs that hinder accurate variant calling. The QResolve project will translate these computationally intensive tasks into forms suitable for both classical and quantum optimisation algorithms, building on preliminary work already performed on IBM quantum processors with 24 to 48 qubits.
This effort will specifically validate its methods against Genome in a Bottle datasets, aiming to deliver a Sarek-compatible prototype module for wider use in regulatory genomics workflows. The project expects to create a decision framework identifying when classical, annealing, or gate-based quantum approaches are most effective.
The QResolve project translates complex genomic problems into Quadratic Unconstrained Binary Optimisation (QUBO) and Higher-Order Unconstrained Binary Optimisation (HUBO) formulations, allowing evaluation on both classical and quantum systems. Biological validation will specifically use Genome in a Bottle datasets, providing a standardized benchmark for assessing the accuracy of quantum-derived genomic assemblies. Researchers plan to integrate their quantum optimization methods into Sarek, a widely-used genomic analysis tool, creating a prototype module for direct application within existing workflows.
Performance comparisons between quantum and classical approaches will be conducted using advanced optimization techniques and quantum simulators before moving to available gate-based quantum hardware. A reusable genomic graph benchmark suite will also be created, furthering the development of standardized evaluation metrics for this emerging field.
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