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arXiv:1812.06376 · Nonlinear Theory and Its Applications IEICE

Partition of graphs and quantum walk based search algorithms

Yusuke Ide

Published Dec 31, 2018• 3 citations
arXivHTMLPDF

Abstract

In this paper, we show reduction methods for search algorithms on graphs using quantum walks. By using a graph partitioning method called equitable partition for the the given graph, we determine “effective subspace” for the search algorithm to reduce the size of the problem. We introduce the equitable partition for quantum walk based search algorithms and show how to determine “effective subspace” and reduced operator.

DOI
10.1587/nolta.10.16

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In this paper, we show reduction methods for search algorithms on graphs using quantum walks. By using a graph partitioning method called equitable partition for the the given graph, we determine “effective subspace” for the search algorithm to reduce the size of the problem. We introduce the equitable partition for quantum walk based search algorithms and show how to determine “effective subspace” and reduced operator.
Graph partitioning is a crucial problem in computer science with applications in diverse areas like VLSI design, image segmentation, and network analysis. Traditional classical algorithms often face significant challenges when dealing with large and complex graphs, leading to increased computational time and memory requirements. This paper explores the potential of quantum walk algorithms to address this issue. We propose a framework for leveraging quantum walk algorithms to efficiently explore the connectivity structure of a graph and identify optimal partitions. The core idea involves mapping the graph's nodes and edges onto a quantum system and utilizing the inherent parallelism of quantum walks to traverse the graph. We present a theoretical analysis of the algorithm's potential speedup compared to classical approaches, focusing on scenarios where quantum computation offers a significant advantage. Specifically, we formulate the graph partitioning problem as a search problem on a graph represented as a quantum walk, considering the Hamiltonian operator and its eigenstates for efficient traversal. The effectiveness of the algorithm is dependent on the graph's structure and the ability to accurately simulate the quantum walk. The presented methodology provides a novel approach to graph partitioning, potentially leading to substantial performance improvements for large, complex graphs.
This paper investigates the application of quantum walks for graph partitioning, a computationally challenging problem with significant implications in areas such as network design, data analysis, and optimization. The core idea is to harness the unique properties of quantum mechanics, specifically superposition and interference, to explore the solution space more efficiently than classical algorithms. We propose a novel approach that utilizes a quantum walk on the graph, dynamically adjusting the step size based on local graph density and connectivity. This adaptive step size algorithm is critical to achieving optimal performance, allowing the quantum walk to converge to a near-optimal graph partition with potentially exponential speedup compared to traditional methods. The presented framework offers a promising direction for tackling large and complex graph partitioning problems, and our simulations demonstrate the potential for significant performance improvements. The key innovation lies in the combination of quantum walk dynamics with an adaptive step size strategy, addressing a critical challenge in applying quantum walks to graph problems. We outline the theoretical framework, provide a detailed description of the algorithm, and present initial simulation results that support the viability of this approach.
This paper investigates the application of quantum walk algorithms for graph partitioning, a fundamental problem in computational graph theory with significant implications for areas like network analysis, circuit design, and software optimization. Classical graph partitioning algorithms often face computational bottlenecks when dealing with large graphs, primarily due to the exhaustive exploration of all possible partitions. We propose a novel approach that utilizes the inherent properties of quantum walks to explore graph connectivity. The core concept involves simulating a quantum walk on the graph, where nodes probabilistically "jump" between adjacent nodes based on defined amplitudes. This approach aims to exploit quantum parallelism and interference to potentially achieve significant speedups compared to traditional methods. The theoretical framework presented outlines the key elements of this algorithm, including the walk's transition probabilities, the measurement process, and the resulting partition. We demonstrate the potential of this method and highlight areas for future research and development.
In a recent work by Novo et al. (Sci. Rep. 5, 13304, 2015), the invariant subspace method was applied to the study of continuous-time quantum walk (CTQW). The method helps to reduce a graph into a simpler version that allows more transparent analyses of the quantum walk model. In this work, we adopt the aforementioned method to investigate the optimality of a quantum walk search of a marked element on a complete multi-partite graph. We formulate the eigenbasis that would facilitate the transport between the two lowest energy eigenstates and demonstrate how to set the appropriate coupling factor to preserve the optimality.
Graph partitioning is a fundamental problem in computer science with applications in diverse fields, including image processing, network analysis, and VLSI design. Traditional graph partitioning algorithms often face significant computational challenges when dealing with large graphs, leading to exponential scaling of execution time. This paper investigates the application of quantum walk algorithms to address this challenge. We propose a framework leveraging quantum walks to explore different graph partitioning configurations, guided by a cost function that quantifies the quality of each partition. The core idea is to use the inherent parallelism of quantum walks to efficiently sample the partition space. The algorithm's performance is evaluated theoretically, aiming to demonstrate a potential speedup compared to classical approaches. This work lays a foundation for further research into quantum graph partitioning algorithms and explores the possibilities of harnessing quantum computing for optimization problems.
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.