Scaling Quantum Optimisation Beyond Hardware Limits for Real-World Scientific Workloads: Genome Assembly on Current Quantum Hardware

Genome assembly plays a crucial role in combating infectious diseases, monitoring antimicrobial resistance, and studying cancer genomics. This complex task of reconstructing complete genomic sequences from fragmented DNA reads can be viewed as a large-scale combinatorial optimization problem. Recent advancements in quantum computing have introduced innovative optimization algorithms that hold promise for tackling these intricate search spaces more efficiently.

However, the practical implementation of these quantum algorithms is currently constrained by the limitations of noisy intermediate-scale quantum (NISQ) hardware. Such hardware comes with restrictions such as limited qubit counts, limited connectivity, and high error rates, which impede their widespread application.

To overcome these limitations, researchers have turned to the Hamiltonian Auto Decomposition Optimisation Framework (HADOF), an algorithm-agnostic framework designed to facilitate scalable quantum optimization through the concept of federated solving, which involves breaking down complex problems into smaller, more manageable subproblems.

Utilizing HADOF, the team successfully executed quantum-assisted genome assembly on a particularly challenging dataset: a 7.1 million base pair genome of the bacterium Pseudomonas aeruginosa. This accomplishment marks a significant milestone as it represents the largest genome assembly graph analyzed on actual quantum hardware to date.

The results were impressive, with a remarkable 99.348% genome fraction achieved, alongside a 1.0 duplication ratio. These findings affirm that despite the current constraints of quantum hardware, it is indeed possible to obtain biologically plausible genome reconstructions.