Quantum Bioinformatics: Gene Sequence Alignment Based on Quantum Entanglement

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel approach to gene sequence alignment leveraging the principles of quantum entanglement. Traditional gene sequence alignment methods face significant computational challenges, particularly with increasing genome sizes, leading to slow processing times and limitations in accuracy. Our proposed methodology encodes gene sequences into qubits and utilizes quantum entanglement to establish correlations between sequences, dramatically accelerating the alignment process. We present a theoretical framework outlining the core claim, mechanism, and potential advantages of this approach. The theoretical model focuses on exploiting quantum superposition and entanglement to explore the vast solution space of possible alignments concurrently, offering a significant speedup compared to classical algorithms. We detail the mathematical formulation of the entanglement-based alignment process, including the key equations governing the correlation probability and the estimated reduction in computational complexity. The core aim is to demonstrate the feasibility of using quantum entanglement to enhance gene sequence alignment, potentially revolutionizing bioinformatics research and accelerating the discovery of biological insights.

DOI: 10.5281/zenodo.22268593

Related papers

Quantum Bioinformatics

Jincheng Zhang

This paper explores the emerging field of quantum bioinformatics, proposing a novel approach to tackling complex challenges within biological information analysis. Traditional biological data processing often encounters limitations due to the exponential growth of datasets and the computational intensity of tasks like sequence alignment, gene prediction, and protein structure prediction. We posit that quantum computing, leveraging phenomena like superposition and entanglement, offers a paradigm shift capable of dramatically accelerating these calculations. This document details the core concept of representing biological sequences as quantum states and utilizing quantum algorithms – specifically tailored for bioinformatics applications – to perform these computations. The theoretical framework incorporates key quantum principles to enhance efficiency and potentially unlock new insights in biological systems. We present a foundational model for quantum sequence alignment, quantum gene prediction, and quantum protein structure prediction, outlining the mathematical formulations and algorithmic approaches. The potential impact of this work is significant, suggesting a future where quantum bioinformatics becomes a cornerstone of biological research. ---

Sep 5, 20260 citations

Bioinformatics Quantum Algorithm

Jincheng Zhang

This paper explores the potential of quantum computing to revolutionize bioinformatics, specifically focusing on enhanced gene sequence analysis and protein structure prediction. The core concept centers around utilizing quantum error correction and quantum computation to accelerate these computationally intensive tasks. We propose a novel algorithm architecture leveraging quantum entanglement and superposition to overcome limitations inherent in classical approaches. The paper details the design of a quantum algorithm for sequence alignment and protein folding, emphasizing the key quantum mechanisms that underpin its enhanced performance. We present a preliminary analysis of the algorithm's potential advantages, focusing on reduced computational complexity and improved accuracy compared to existing classical methods. The research highlights the significance of quantum algorithms in addressing critical challenges within the field of bioinformatics.

Aug 31, 20260 citations

Quantum Computing Genome Sequence Prediction

Jincheng Zhang

Quantum Computing Genome Sequence Prediction investigates the potential of quantum computing to revolutionize gene sequencing analysis. Traditional methods face computational bottlenecks, hindering the accurate prediction of genetic sequences. This research explores the application of quantum algorithms to accelerate the prediction process, ultimately aiming to improve the accuracy and efficiency of gene sequence analysis. The core claim centers on leveraging quantum computation's unique properties to overcome limitations in classical algorithms and achieve superior prediction results. The investigation will detail a novel quantum approach to sequence prediction, focusing on reducing computational complexity and enhancing prediction fidelity. This paper will present a comprehensive overview of the proposed methodology, including mathematical formulations, key algorithmic steps, and potential performance benchmarks. The study will also address challenges related to quantum hardware limitations and the integration of existing bioinformatics tools.

Sep 2, 20260 citations

Quantum Biology-Inspired Bioinformatics Algorithms

Jincheng Zhang

This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---

Aug 29, 20260 citations

Quantum Bioinformatics: Gene Sequence Analysis Based on Quantum Error Correction Codes

Jincheng Zhang

This paper explores the application of quantum error correction codes (QECCs) to gene sequence analysis, addressing the limitations of traditional methods in handling high error rates and complex interactions within genomic data. We posit that gene sequences can be represented as quantum bits (qubits), and QECCs can be utilized to identify redundant information and uncover hidden patterns indicative of biological function. The core claim centers on leveraging the high error-correcting capabilities of QECCs to analyze gene sequences with significant redundancy, revealing intricate patterns and interactions previously inaccessible through conventional approaches. The proposed mechanism involves encoding gene sequences into a qubit representation and employing QECC algorithms for error detection and correction, ultimately leading to a refined understanding of genomic data. This research introduces a novel framework for gene sequence analysis, potentially accelerating discoveries in areas such as personalized medicine and evolutionary biology. The core mechanism relies on the ability of QECCs to effectively mitigate errors arising from inherent sequence variations and complex biological processes, providing a robust and potentially highly accurate method for genomic investigation. The analysis presented utilizes a simplified model for demonstration, with future work focused on adapting the techniques to larger and more complex genomic datasets.

Aug 29, 20260 citations

Convergence of Bioinformatics and Quantum Computing: A Novel Framework for Genome Sequencing Acceleration

Jun‐Han Huang, Fabrizio Falchi, Eneko Osaba

The exponential growth of genomic data presents significant computational challenges in the field of bioinformatics, particularly in genome sequencing. Traditional methods struggle to meet the increasing demand for speed, accuracy, and efficiency. This paper introduces a novel framework that leverages quantum computing to accelerate genome sequencing processes. The proposed framework integrates quantum algorithms, such as Grover's search and quantum Fourier transform, with classical bioinformatics techniques to optimize key tasks, including sequence alignment, variant detection, and large-scale data analysis. By exploiting the inherent parallelism and computational power of quantum systems, the framework achieves substantial reductions in processing time while maintaining high accuracy. A case study on large-scale genomic datasets demonstrates the framework's ability to outperform state-of-the-art classical approaches, particularly in handling complex and large-scale sequencing tasks. Furthermore, this research highlights the potential of quantum computing in advancing bioinformatics, paving the way for more efficient and scalable solutions to address the computational bottlenecks of modern genomics. The implications of this interdisciplinary approach extend beyond genomics, offering transformative possibilities in data-driven life sciences.

International Journal of Computer Engineering in Research TrendsAug 31, 20240 citations