Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

VRP Assembler: haplotype-resolved de novo assembly of diploid and polyploid genomes using quantum computing

Yibo Chen, Jun-Han Huang, Yuhui Sun, Yong Zhang, Yuxiang Li, Xun Xu

Published Oct 21, 2023• 0 citations
Publisher

Abstract

Abstract Precision medicine’s emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google’s OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

DOI
10.1101/2023.10.19.563028

Related papers

Haplotype-resolved assembly of diploid and polyploid genomes using quantum computing

Yibo Chen, Jun-Han Huang, Yuhui Sun +3 more

Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.

Cell Reports Methods•Apr 12, 2024•10 citations

QHap: Quantum-Inspired Haplotype Phasing

Rui Zhang, Xian-Zhe Tao, Yibo Chen +12 more

Haplotype phasing, the process of resolving parental allele inheritance patterns in diploid genomes, is critical for precision medicine and population genetics, yet the underlying optimization is NP-hard, posing a scalability challenge. To address this, we introduce QHap, a haplotype phasing algorithm that leverages quantum-annealing-inspired optimization. By reformulating haplotype phasing as a Max-Cut problem and deploying a GPU-accelerated ballistic simulated bifurcation solver, QHap accelerates phasing while maintaining accuracy comparable to established phasing tools. On the highly polymorphic human major histocompatibility complex region, QHap demonstrates 4- to 20-fold acceleration over HapCUT2 and WhatsHap with zero switch error across multiple long-read sequencing platforms. The framework implements two strategies: a read-based method for regional phasing, and a single nucleotide polymorphism-based method that, through quality-weighted probabilistic edge construction, efficiently scales to chromosome-scale tasks. Integration of Pore-C chromatin conformation capture data increases the haplotype N50 by up to 15-fold, enabling near-chromosome-scale haplotype reconstruction. QHap demonstrates that quantum-inspired algorithms operating on classical hardware offer a promising approach to addressing the growing computational demands of sequencing data, establishing a new paradigm for applying physics-inspired optimization to fundamental challenges in computational genomics.

q-bio.GNCross-listedarXiv:2603.25762•Mar 27, 2026

VRP Assembler: Haplotype-Resolved de novo Assembly of Diploid and Polyploid Genomes Using Quantum Computing

Yibo Chen, Jun-Han Huang, Yuhui Sun +3 more

•Jan 1, 2023•2 citations

Algorithm for DNA sequence assembly by quantum annealing

Katarzyna Nałęcz-Charkiewicz, Robert Marek Nowak

BACKGROUND: The assembly task is an indispensable step in sequencing genomes of new organisms and studying structural genomic changes. In recent years, the dynamic development of next-generation sequencing (NGS) methods raises hopes for making whole-genome sequencing a fast and reliable tool used, for example, in medical diagnostics. However, this is hampered by the slowness and computational requirements of the current processing algorithms, which raises the need to develop more efficient algorithms. One possible approach, still little explored, is the use of quantum computing. RESULTS: We present a proof of concept of de novo assembly algorithm, using the Genomic Signal Processing approach, detecting overlaps between DNA reads by calculating the Pearson correlation coefficient and formulating the assembly problem as an optimization task (Traveling Salesman Problem). Computations performed on a classic computer were compared with the results achieved by a hybrid method combining CPU and QPU calculations. For this purpose quantum annealer by D-Wave was used. The experiments were performed with artificially generated data and DNA reads coming from a simulator, with actual organism genomes used as input sequences. To our knowledge, this work is one of the few where actual sequences of organisms were used to study the de novo assembly task on quantum annealer. CONCLUSIONS: Proof of concept carried out by us showed that the use of quantum annealer (QA) for the de novo assembly task might be a promising alternative to the computations performed in the classical model. The current computing power of the available devices requires a hybrid approach (combining CPU and QPU computations). The next step may be developing a hybrid algorithm strictly dedicated to the de novo assembly task, using its specificity (e.g. the sparsity and bounded degree of the overlap-layout-consensus graph).

BMC Bioinformatics•Apr 7, 2022•37 citations

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

Jaya Vasavi Pamidimukkala, Himanshu Sahu, Ashwini Kannan +3 more

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.

quant-pharXiv:2602.00156•Feb 2, 2026•1 citation

Genome assembly using quantum and quantum-inspired annealing

Aleksey S. Boev, Alexander Rakitko, Sergey R. Usmanov +5 more

Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for de novo genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the [Formula: see text]X 174 bacteriophage. Our results pave a way for a significant increase in the efficiency of solving bioinformatics problems with the use of quantum computing technologies and, in particular, quantum annealing might be an effective method. We expect that the new generation of quantum annealing devices would outperform existing techniques for de novo genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.

arXiv:2004.06719•Scientific Reports•Jun 23, 2021•63 citations
Abstract Precision medicine’s emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the VRP assembler: a novel approach that transforms this task into a vehicle routing problem, a combinatorial optimization problem solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google’s OR-Tools, achieving a haplotype-resolved assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to hifiasm with the phasing accuracy first approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.
Precision medicine's emphasis on individual genetic variants highlights the importance of haplotype-resolved assembly, a computational challenge in bioinformatics given its combinatorial nature. While classical algorithms have made strides in addressing this issue, the potential of quantum computing remains largely untapped. Here, we present the vehicle routing problem (VRP) assembler: an approach that transforms this task into a vehicle routing problem, an optimization formulation solvable on a quantum computer. We demonstrate its potential and feasibility through a proof of concept on short synthetic diploid and triploid genomes using a D-Wave quantum annealer. To tackle larger-scale assembly problems, we integrate the VRP assembler with Google's OR-Tools, achieving a haplotype-resolved local assembly across the human major histocompatibility complex (MHC) region. Our results show encouraging performance compared to Hifiasm with phasing accuracy approaching the theoretical limit, underscoring the promising future of quantum computing in bioinformatics.
Haplotype phasing, the process of resolving parental allele inheritance patterns in diploid genomes, is critical for precision medicine and population genetics, yet the underlying optimization is NP-hard, posing a scalability challenge. To address this, we introduce QHap, a haplotype phasing algorithm that leverages quantum-annealing-inspired optimization. By reformulating haplotype phasing as a Max-Cut problem and deploying a GPU-accelerated ballistic simulated bifurcation solver, QHap accelerates phasing while maintaining accuracy comparable to established phasing tools. On the highly polymorphic human major histocompatibility complex region, QHap demonstrates 4- to 20-fold acceleration over HapCUT2 and WhatsHap with zero switch error across multiple long-read sequencing platforms. The framework implements two strategies: a read-based method for regional phasing, and a single nucleotide polymorphism-based method that, through quality-weighted probabilistic edge construction, efficiently scales to chromosome-scale tasks. Integration of Pore-C chromatin conformation capture data increases the haplotype N50 by up to 15-fold, enabling near-chromosome-scale haplotype reconstruction. QHap demonstrates that quantum-inspired algorithms operating on classical hardware offer a promising approach to addressing the growing computational demands of sequencing data, establishing a new paradigm for applying physics-inspired optimization to fundamental challenges in computational genomics.
BACKGROUND: The assembly task is an indispensable step in sequencing genomes of new organisms and studying structural genomic changes. In recent years, the dynamic development of next-generation sequencing (NGS) methods raises hopes for making whole-genome sequencing a fast and reliable tool used, for example, in medical diagnostics. However, this is hampered by the slowness and computational requirements of the current processing algorithms, which raises the need to develop more efficient algorithms. One possible approach, still little explored, is the use of quantum computing. RESULTS: We present a proof of concept of de novo assembly algorithm, using the Genomic Signal Processing approach, detecting overlaps between DNA reads by calculating the Pearson correlation coefficient and formulating the assembly problem as an optimization task (Traveling Salesman Problem). Computations performed on a classic computer were compared with the results achieved by a hybrid method combining CPU and QPU calculations. For this purpose quantum annealer by D-Wave was used. The experiments were performed with artificially generated data and DNA reads coming from a simulator, with actual organism genomes used as input sequences. To our knowledge, this work is one of the few where actual sequences of organisms were used to study the de novo assembly task on quantum annealer. CONCLUSIONS: Proof of concept carried out by us showed that the use of quantum annealer (QA) for the de novo assembly task might be a promising alternative to the computations performed in the classical model. The current computing power of the available devices requires a hybrid approach (combining CPU and QPU computations). The next step may be developing a hybrid algorithm strictly dedicated to the de novo assembly task, using its specificity (e.g. the sparsity and bounded degree of the overlap-layout-consensus graph).
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.
Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for de novo genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the [Formula: see text]X 174 bacteriophage. Our results pave a way for a significant increase in the efficiency of solving bioinformatics problems with the use of quantum computing technologies and, in particular, quantum annealing might be an effective method. We expect that the new generation of quantum annealing devices would outperform existing techniques for de novo genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.