Nonvariational quantum optimisation approaches to pangenome-guided sequence assembly

TL;DR

This paper develops quantum optimisation methods for pangenome-guided sequence assembly, reducing problem complexity and demonstrating near-term quantum hardware's potential in solving biologically relevant genome assembly tasks.

Contribution

It introduces a new higher-order binary optimisation formulation and a quantum iterative approach tailored for current quantum devices to address genome assembly challenges.

Findings

Iterative-QAOA reliably finds optimal assemblies in noiseless simulations.

Quantum hardware closely reproduces simulation results with sufficient sampling.

The HUBO formulation reduces variables from O(N^2) to O(N log N).

Abstract

Assembling genomes from short-read sequencing data remains difficult in repetitive regions, where reference bias and combinatorial complexity limit existing methods. Pangenome-guided sequence assembly (PGSA) mitigates reference bias by reconstructing an individual genome as a walk through a population-level graph. The associated problem, identifying a walk whose node visits match read-derived copy numbers, is NP-hard and already challenges classical solvers at a moderate scale. We develop near-term quantum optimisation approaches for this computational bottleneck. We consider two problem encodings: an established quadratic unconstrained binary optimisation and a new higher-order binary optimisation (HUBO) formulation. The latter reduces the number of variables from $O(N^2)$ to $O(N\log N)$ and places moderate-sized instances within the qubit budget of current devices. We solve both using the Iterative-QAOA framework, which combines a fixed linear-ramp QAOA schedule with iterative warm-start bias updates, avoiding the overhead of full variational parameter optimisation. A custom circuit compilation strategy reduces hardware gate overhead by up to 67\% compared with standard tools. In noiseless simulations of QUBO problems, Iterative-QAOA reliably identifies optimal assemblies from as few as $10^{-17}\%$ of all candidate solutions, and \textit{IBM} quantum hardware closely reproduces relevant results with sufficient sampling via CVaR-style post-selection. For HUBO, the variable reduction comes at the cost of deeper compiled circuits and greater noise sensitivity: an expected qubit--depth trade-off. Our findings establish pangenome assembly as a concrete, biologically motivated problem class at the scale where quantum optimisation may first provide practical value.