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

Frontiers in Bioinformatics

The quantum hypercube as a k-mer graph

Gustavo Becerra-Gaviño, Liliana Ibeth Barbosa-Santillán

Published Sep 12, 2024• 1 citation
Publisher

Abstract

The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.

DOI
10.3389/fbinf.2024.1401223

Related papers

Quantum Walk-Based Algorithms for Large-Scale Graph Search

Jincheng Zhang

This paper investigates the application of quantum walk algorithms to large-scale graph search problems. Classical algorithms often struggle with the computational complexity of exploring vast graphs, particularly when searching for specific patterns or nodes. Quantum walks, inspired by the probabilistic nature of random walks, provide a promising alternative by utilizing quantum superposition and interference to accelerate the search process. We explore the theoretical framework and potential advantages of employing quantum walks for graph traversal, focusing on scenarios where exponential speedups are theoretically possible. This work aims to demonstrate the potential of quantum computing to tackle computationally demanding graph search tasks that are currently intractable for classical methods. The core concept leverages the ability to simultaneously explore multiple paths within a graph, drastically reducing the search time compared to sequential exploration. We present a foundational understanding of quantum walk implementation and analyze its suitability for various graph search algorithms.

•Aug 28, 2026•0 citations

Quantum Walk-Based Algorithms for Graph Traversal and Search

Jincheng Zhang

This paper explores the application of quantum walk algorithms to graph traversal and search problems. Classical algorithms often struggle with the computational complexity of traversing and searching large graphs, leading to significant time and resource limitations. Quantum walks, utilizing superposition and interference, offer a potential pathway to achieve exponential speedups. This work details the theoretical framework for applying quantum walks to graph problems, specifically focusing on traversal and search tasks. We present a generalized approach for mapping graph structures onto quantum walk systems and discuss the key mathematical formulations involved. The core of our investigation revolves around the construction of quantum walk operators that mimic the movement and interaction rules inherent in graph traversal. We analyze the evolution of the quantum walk state and derive expressions for the probability of reaching a specific node in the graph. Furthermore, we examine the impact of graph topology on the performance of the quantum walk, highlighting the potential for tailored algorithms to exploit specific graph structures. The results demonstrate the theoretical feasibility of quantum walk algorithms for significantly accelerating graph traversal and search operations, contributing to advancements in areas such as network analysis, database management, and artificial intelligence.

•Sep 6, 2026•0 citations

Quantum-walk-based search algorithms with multiple marked vertices

Gustavo Alves Bezerra, Pedro H. G. Lugão, Renato Portugal

The quantum walk is a powerful tool to develop quantum algorithms, which usually are based on searching for a vertex in a graph with multiple marked vertices, with Ambainis's quantum algorithm for solving the element distinctness problem being the most shining example. In this work, we address the problem of calculating analytical expressions of the time complexity of finding a marked vertex using quantum-walk-based search algorithms with multiple marked vertices on arbitrary graphs, extending previous analytical methods based on Szegedy's quantum walk, which can be applied only to bipartite graphs. Two examples based on the coined quantum walk on two-dimensional lattices and hypercubes show the details of our method.

arXiv:2103.12878•Physical Review A•Jun 1, 2021•22 citations

Quantum Walk-Based Graph Exploration for Drug Discovery

Jincheng Zhang

Drug discovery remains a complex and time-consuming process, largely constrained by the vastness of chemical space. Traditional methods often rely on high-throughput screening, which is both expensive and inefficient. This research proposes a novel approach utilizing quantum walks on graph representations of molecules to efficiently explore this chemical space. The core idea involves encoding molecular structures as graphs and leveraging the unique properties of quantum walks – specifically, their ability to simultaneously explore multiple paths – to accelerate the search for potential drug candidates. We formulate the problem as a quantum walk on a graph (G = (V, E)), where V represents the nodes (atoms) and E represents the edges (bonds) of the molecule. The walk is defined by a transition probability matrix P, which is derived from the molecular graph and specifies the likelihood of transitioning between neighboring nodes. The objective is to find the shortest path to a target node, representing a molecule with desired properties, using the quantum walk algorithm. We explore the theoretical possibilities and outline a computational framework. The potential of this approach to dramatically reduce the time and cost associated with drug discovery is highlighted.

•Sep 6, 2026•0 citations

Quantum Walk-Based Optimization for Combinatorial Problems

Jincheng Zhang

This paper explores the application of quantum walks as a novel approach to optimization within the realm of combinatorial problems. The core argument posits that quantum walks, leveraging the principles of quantum mechanics like superposition and interference, can potentially outperform classical walk-based optimization methods, particularly when dealing with complex problem landscapes. We propose a framework where combinatorial problems are represented as graphs and navigated via quantum walks. The implementation utilizes the inherent probabilistic nature of quantum walks to explore the solution space more efficiently through constructive interference, while suppressing destructive interference that would lead to unproductive exploration. We detail the theoretical underpinnings, focusing on the mathematical formulation of the quantum walk and its interaction with the problem graph. The key objective is to demonstrate a potential speedup in convergence compared to traditional optimization techniques. This work provides a foundational exploration, aiming to lay the groundwork for future research and development in this emerging field.

•Aug 31, 2026•0 citations

Quantum Walk-Based Routing Protocols for Networks

Jincheng Zhang

This paper investigates the application of quantum walks to the design of routing protocols for computer networks. Traditional routing algorithms, reliant on classical probability and exhaustive search, often suffer from inefficiencies and vulnerabilities, particularly in large and complex network topologies. Quantum walks, leveraging the principles of superposition and interference, offer a fundamentally different approach. This research proposes a framework for encoding network topologies within a quantum walk environment and utilizing the walk's inherent properties to explore the network and identify optimal paths. The core of the approach lies in mapping network nodes to quantum bits (Qubits) and utilizing the walk's probabilistic nature to simulate path exploration. We explore key aspects of this approach, including walk initialization, boundary conditions, and measurement strategies. The potential benefits of this method, such as reduced latency, improved bandwidth utilization, and enhanced resilience to network disruptions, are discussed. We present a theoretical model for the routing protocol and analyze its performance characteristics, focusing on metrics like path length, traversal time, and success rate. While this work is primarily a theoretical exploration, it lays a foundation for future research and development of practical quantum routing solutions.

•Sep 5, 2026•0 citations
The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.
This paper investigates the application of quantum walk algorithms to large-scale graph search problems. Classical algorithms often struggle with the computational complexity of exploring vast graphs, particularly when searching for specific patterns or nodes. Quantum walks, inspired by the probabilistic nature of random walks, provide a promising alternative by utilizing quantum superposition and interference to accelerate the search process. We explore the theoretical framework and potential advantages of employing quantum walks for graph traversal, focusing on scenarios where exponential speedups are theoretically possible. This work aims to demonstrate the potential of quantum computing to tackle computationally demanding graph search tasks that are currently intractable for classical methods. The core concept leverages the ability to simultaneously explore multiple paths within a graph, drastically reducing the search time compared to sequential exploration. We present a foundational understanding of quantum walk implementation and analyze its suitability for various graph search algorithms.
This paper explores the application of quantum walk algorithms to graph traversal and search problems. Classical algorithms often struggle with the computational complexity of traversing and searching large graphs, leading to significant time and resource limitations. Quantum walks, utilizing superposition and interference, offer a potential pathway to achieve exponential speedups. This work details the theoretical framework for applying quantum walks to graph problems, specifically focusing on traversal and search tasks. We present a generalized approach for mapping graph structures onto quantum walk systems and discuss the key mathematical formulations involved. The core of our investigation revolves around the construction of quantum walk operators that mimic the movement and interaction rules inherent in graph traversal. We analyze the evolution of the quantum walk state and derive expressions for the probability of reaching a specific node in the graph. Furthermore, we examine the impact of graph topology on the performance of the quantum walk, highlighting the potential for tailored algorithms to exploit specific graph structures. The results demonstrate the theoretical feasibility of quantum walk algorithms for significantly accelerating graph traversal and search operations, contributing to advancements in areas such as network analysis, database management, and artificial intelligence.
The quantum walk is a powerful tool to develop quantum algorithms, which usually are based on searching for a vertex in a graph with multiple marked vertices, with Ambainis's quantum algorithm for solving the element distinctness problem being the most shining example. In this work, we address the problem of calculating analytical expressions of the time complexity of finding a marked vertex using quantum-walk-based search algorithms with multiple marked vertices on arbitrary graphs, extending previous analytical methods based on Szegedy's quantum walk, which can be applied only to bipartite graphs. Two examples based on the coined quantum walk on two-dimensional lattices and hypercubes show the details of our method.
Drug discovery remains a complex and time-consuming process, largely constrained by the vastness of chemical space. Traditional methods often rely on high-throughput screening, which is both expensive and inefficient. This research proposes a novel approach utilizing quantum walks on graph representations of molecules to efficiently explore this chemical space. The core idea involves encoding molecular structures as graphs and leveraging the unique properties of quantum walks – specifically, their ability to simultaneously explore multiple paths – to accelerate the search for potential drug candidates. We formulate the problem as a quantum walk on a graph (G = (V, E)), where V represents the nodes (atoms) and E represents the edges (bonds) of the molecule. The walk is defined by a transition probability matrix P, which is derived from the molecular graph and specifies the likelihood of transitioning between neighboring nodes. The objective is to find the shortest path to a target node, representing a molecule with desired properties, using the quantum walk algorithm. We explore the theoretical possibilities and outline a computational framework. The potential of this approach to dramatically reduce the time and cost associated with drug discovery is highlighted.
This paper explores the application of quantum walks as a novel approach to optimization within the realm of combinatorial problems. The core argument posits that quantum walks, leveraging the principles of quantum mechanics like superposition and interference, can potentially outperform classical walk-based optimization methods, particularly when dealing with complex problem landscapes. We propose a framework where combinatorial problems are represented as graphs and navigated via quantum walks. The implementation utilizes the inherent probabilistic nature of quantum walks to explore the solution space more efficiently through constructive interference, while suppressing destructive interference that would lead to unproductive exploration. We detail the theoretical underpinnings, focusing on the mathematical formulation of the quantum walk and its interaction with the problem graph. The key objective is to demonstrate a potential speedup in convergence compared to traditional optimization techniques. This work provides a foundational exploration, aiming to lay the groundwork for future research and development in this emerging field.
This paper investigates the application of quantum walks to the design of routing protocols for computer networks. Traditional routing algorithms, reliant on classical probability and exhaustive search, often suffer from inefficiencies and vulnerabilities, particularly in large and complex network topologies. Quantum walks, leveraging the principles of superposition and interference, offer a fundamentally different approach. This research proposes a framework for encoding network topologies within a quantum walk environment and utilizing the walk's inherent properties to explore the network and identify optimal paths. The core of the approach lies in mapping network nodes to quantum bits (Qubits) and utilizing the walk's probabilistic nature to simulate path exploration. We explore key aspects of this approach, including walk initialization, boundary conditions, and measurement strategies. The potential benefits of this method, such as reduced latency, improved bandwidth utilization, and enhanced resilience to network disruptions, are discussed. We present a theoretical model for the routing protocol and analyze its performance characteristics, focusing on metrics like path length, traversal time, and success rate. While this work is primarily a theoretical exploration, it lays a foundation for future research and development of practical quantum routing solutions.