Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914595819487232 |
|---|---|
| author | Pamidimukkala, Jaya Vasavi Sahu, Himanshu Kannan, Ashwini Ananthanarayanan, Janani Dasgupta, Kalyan Senapati, Sanjib |
| author_facet | Pamidimukkala, Jaya Vasavi Sahu, Himanshu Kannan, Ashwini Ananthanarayanan, Janani Dasgupta, Kalyan Senapati, Sanjib |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00156 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery Pamidimukkala, Jaya Vasavi Sahu, Himanshu Kannan, Ashwini Ananthanarayanan, Janani Dasgupta, Kalyan Senapati, Sanjib Quantum Physics Genomics 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. |
| title | Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery |
| topic | Quantum Physics Genomics |
| url | https://arxiv.org/abs/2602.00156 |