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

TL;DR

This work tackles the computational bottleneck of de novo genome assembly by formulating the Hamiltonian path problem on assembly graphs as a quantum-augmented optimization task. It introduces a Higher-Order Binary Optimization (HOBO) encoding with a CVaR-VQE workflow, complemented by a novel bitstring recovery mechanism to reduce qubit counts to $N log2 N$ and guide the optimizer toward valid assembly paths. Demonstrations on simulators and IBM hardware show feasibility for graphs up to 18 nodes, with partial success on larger graphs, and downstream contig construction enabling organism identification via BLASTn, underscoring potential gains as quantum hardware advances. The framework provides a scalable, hybrid approach to accelerate de novo genome assembly and organism discovery in reference-free contexts.

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.

Figures (8)

• Figure 1: A streamlined pipeline for efficient genome sequencing using hybrid classical-quantum methods.
• Figure 2: A demonstration of the self-consistent recovery mechanism for bitstrings applied to an example sequence containing repetitions and invalid nodes (anomalies)
• Figure 3: Visualization of qubit encodings for the genome sequencing problem. (a) An example graph where black edges represents the connected nodes, while gray edges shows unconnected nodes. (b) QUBO encoding (c) HOBO encoding
• Figure 4: Illustration of three ansatz used in variational quantum design. The quantum gate $\mathcal{U}$ represents single qubit rotation gate with angles $(\theta_x,\theta_y,\theta_z)$. The orange color blocks shows qubits associated with $b_t$ representing particular position or time. Left :The simplest possibility is to consider ansatz which consists of only single qubit rotation gates, therefore, there is no entanglement and the total state $|\Psi\rangle$ remain in product state of individual qubits. Centre : A non-trivial ansatz considers entanglement (CNOT gates) only among qubits ($\log_2 N$ qubits) representing a particular position, in a linear fashion. Right : Ansatz with linear entanglement spanning across all qubits, akin to an EfficentSU2 ansatz.
• Figure 5: Representative solution for a 6-node system