Scaling Quantum Optimisation Beyond Hardware Limits for Real-World Scientific Workloads: Genome Assembly on Current Quantum Hardware
Genome assembly is important in infectious disease surveillance, antimicrobial resistance monitoring, and cancer genomics. The task of reconstructing full genomic sequences from fragmented reads, can be framed as a large scale combinatorial optimisation problem. Recent advances in quantum computing have introduced new optimisation algorithms with potential advantages for navigating complex combinatorial search spaces. However, practical deployment is limited by noisy intermediate-scale quantum (NISQ) hardware, including restricted qubit counts, limited connectivity, and high error rates. In this research, we employ the Hamiltonian Auto Decomposition Optimisation Framework (HADOF), an algorithm agnostic framework that enables scalable quantum optimisation through federated solving across small subproblems. HADOF enabled the quantum-assisted genome assembly of a 7.1 Million base pairs Pseudomonas aeruginosa genome, to our knowledge, representing the largest genome assembly graph studied on real quantum hardware to date. The results achieved a 99.348% genome fraction and 1.0 duplication ratio, demonstrating that biologically plausible genome reconstructions can be obtained despite current hardware limitations.