Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery

TL;DR

This paper introduces a hybrid quantum-classical method for de novo genome assembly, leveraging quantum optimization algorithms to potentially improve speed and accuracy in constructing genomes from scratch.

Contribution

It presents a novel quantum-assisted approach using HOBO and VQE to solve assembly graph problems, with a new bitstring recovery mechanism for enhanced solution traversal.

Findings

Quantum approach shows promise in reducing assembly time.

Potential for improved accuracy with advancing quantum hardware.

Demonstrates feasibility of quantum algorithms in complex genomic tasks.

Abstract

Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine. A critical step in any genome sequencing technique is genome assembly. However, de novo genome assembly, which involves constructing an entire genome sequence from scratch without a reference genome, presents significant challenges due to its high computational complexity, affecting both time and accuracy. In this study, we propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process. Specifically, we present a method to solve the Hamiltonian and Eulerian paths within the genome assembly graph using gate-based quantum computing through a Higher-Order Binary Optimization (HOBO) formulation with the Variational Quantum Eigensolver algorithm (VQE), in addition to a novel bitstring recovery mechanism to improve optimizer traversal of the solution space. A comparative analysis with classical optimization techniques was performed to assess the effectiveness of our quantum-based approach in genome assembly. The results indicate that, as quantum hardware continues to evolve and noise levels diminish, our formulation holds a significant potential to accelerate genome sequencing by offering faster and more accurate solutions to the complex challenges in genomic research.