Genome assembly using quantum and quantum-inspired annealing

• URL: http://arxiv.org/abs/2004.06719v3
• Date: Thu, 24 Jun 2021 08:34:52 GMT
• Title: Genome assembly using quantum and quantum-inspired annealing
• Authors: A.S. Boev, A.S. Rakitko, S.R. Usmanov, A.N. Kobzeva, I.V. Popov, V.V. Ilinsky, E.O. Kiktenko, and A.K. Fedorov
• Abstract summary: We demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Our results pave a way for an increase in the efficiency of solving bioinformatics problems with the use of quantum computing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 {\it 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 $\phi$X 174 bacteriophage. Our results pave a way for an increase in the efficiency of solving bioinformatics problems with the use of quantum computing and, in particular, quantum annealing. We expect that the new generation of quantum annealing devices would outperform existing techniques for {\it 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.

Related papers

• Accelerating De Novo Genome Assembly via Quantum-Assisted Graph Optimization with Bitstring Recovery [0.0]
  Genome sequencing is essential to decode genetic information, identify organisms, understand diseases and advance personalized medicine.<n>De novo genome assembly presents significant challenges due to its high computational complexity, affecting both time and accuracy.<n>We propose a hybrid approach utilizing a quantum computing-based optimization algorithm integrated with classical pre-processing to expedite the genome assembly process.
  arXiv  Detail & Related papers  (2026-01-29T19:03:55Z)
• Quantum Network-Based Prediction of Cancer Driver Genes [0.0]
  We introduce a supervised quantum framework that combines mutation scores with network topology via a novel state preparation scheme.<n>QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors, and encodes this information into quantum states.<n> Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline.
  arXiv  Detail & Related papers  (2025-10-14T15:25:30Z)
• Quantum Ensembling Methods for Healthcare and Life Science [0.6386874708851962]
  We study how effective quantum ensemble models are when trained on small data problems in healthcare and life sciences.<n>We constructed multiple types of quantum ensembles for binary classification using up to 26 qubits in simulation and 56 qubits on quantum hardware.<n>We tested these quantum ensembles on synthetic datasets and gene expression data from renal cell carcinoma patients with the task of predicting patient response to immunotherapy.
  arXiv  Detail & Related papers  (2025-06-02T19:54:51Z)
• Quantum-Enhanced Classification of Brain Tumors Using DNA Microarray Gene Expression Profiles [0.0]
  A novel model called "Deep VQC" is proposed, based on the Variational Quantum approach.<n>The model successfully classified four different types of brain tumors-ependymoma, glioblastoma, medulloblastoma, and pilocytic astrocytoma-alongside healthy samples.
  arXiv  Detail & Related papers  (2025-05-04T08:43:31Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data [0.0]
  We focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility.
  arXiv  Detail & Related papers  (2024-11-11T15:34:53Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence [0.0]
  We show that some quantum variants outperform all classical ones in convergence speed towards a near-to-optimal result. If this advantage holds for larger systems, quantum genetic algorithms would provide a new tool to address optimization problems with quantum computers.
  arXiv  Detail & Related papers  (2022-07-19T13:07:44Z)
• Graph Neural Networks for Microbial Genome Recovery [64.91162205624848]
  We propose to use Graph Neural Networks (GNNs) to leverage the assembly graph when learning contig representations for metagenomic binning. Our method, VaeG-Bin, combines variational autoencoders for learning latent representations of the individual contigs, with GNNs for refining these representations by taking into account the neighborhood structure of the contigs in the assembly graph.
  arXiv  Detail & Related papers  (2022-04-26T12:49:51Z)
• Quantum Genetic Algorithm with Individuals in Multiple Registers [0.0]
  We propose a subroutine-based quantum genetic algorithm with individuals codified in independent registers. This distinctive codification allows our proposal to depict all the fundamental elements characterizing genetic algorithms. We study two paradigmatic examples, namely, the biomimetic cloning of quantum observables and the Buv zek-Hillery universal quantum cloning machine.
  arXiv  Detail & Related papers  (2022-03-28T19:05:03Z)
• An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks [27.169643615242652]
  This work introduces a new hybrid quantum kernel support vector machine (QKSVM) combined with a Binary Harris hawk optimization (BHHO) based gene selection for cancer classification on a quantum simulator. The proposed approach is applied to colon and breast microarray datasets and evaluated with all genes and the selected genes by BHHO.
  arXiv  Detail & Related papers  (2022-02-24T04:51:05Z)
• Deep metric learning improves lab of origin prediction of genetically engineered plasmids [63.05016513788047]
  Genetic engineering attribution (GEA) is the ability to make sequence-lab associations. We propose a method, based on metric learning, that ranks the most likely labs-of-origin. We are able to extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.
  arXiv  Detail & Related papers  (2021-11-24T16:29:03Z)
• Estimating gate-set properties from random sequences [0.0]
  Current quantum devices are only capable of short unstructured gate sequences followed by native measurements. A single experiment - random sequence estimation - solves a wealth of estimation problems. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees.
  arXiv  Detail & Related papers  (2021-10-25T18:01:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.