Optimization Algorithms Based on Graph Theory for Quantum Computing

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum computing offers the potential for revolutionary advancements across diverse fields, demanding efficient algorithms and hardware architectures. Traditional optimization methods often overlook the inherent structural properties of quantum circuits, hindering their potential for optimal performance. This paper proposes a novel optimization algorithm based on graph theory, designed to leverage the structural characteristics of quantum circuits to enhance efficiency. The core mechanism involves representing quantum circuits as graphs, utilizing graph properties to guide the optimization process. We explore the application of this approach to optimize quantum circuit parameters, focusing on minimizing circuit depth and improving fidelity. The paper details the algorithm's architecture, the mathematical underpinnings of its optimization strategy, and preliminary results demonstrating its effectiveness in optimizing quantum circuits. The research highlights the potential of graph-based optimization to accelerate the development of practical quantum algorithms.

DOI: 10.5281/zenodo.22186978

Related papers

基于图的量子计算的拓扑结构

Jincheng Zhang

This paper explores the application of graph-based quantum computing frameworks, focusing on the utilization of graph topology for describing quantum circuit operations and performance optimization. We propose a novel approach to quantum algorithm design, leveraging graph structures to enhance computational efficiency and explore potential advancements in quantum computation. The core mechanism involves constructing quantum circuits as graphs, then utilizing graph topology to guide the design process, thereby improving the overall performance. We investigate the implications of this methodology within the context of quantum algorithms and outline future research directions.

Aug 29, 20260 citations

Graph-Based Quantum Computation Algorithm

Jincheng Zhang

This paper presents a novel quantum computation algorithm based on graph theory, aiming to simplify quantum computations by leveraging graph structure characteristics. Traditional quantum computing often struggles with efficiently utilizing graph structures, leading to complex algorithms. Our approach transforms the core quantum computation process into a graph representation, employing graph nodes and edges to design an optimized quantum computation. The core mechanism focuses on reducing computational complexity through the intelligent design of the graph structure itself. This paper details the algorithm's architecture, the rationale behind the graph representation, and the resulting efficiency gains compared to traditional methods. We present preliminary results demonstrating the effectiveness of this algorithm in solving a specific benchmark problem, highlighting its potential for advancing quantum computing.

Sep 2, 20260 citations

Topological Information Graphs for Quantum Algorithm Optimization

Jincheng Zhang

Quantum computing promises revolutionary advancements across numerous fields, but realizing this potential faces significant challenges, primarily related to algorithm design and optimization. Traditional approaches often rely on static parameter settings, limiting the potential for performance gains. This paper introduces a novel quantum algorithm optimization strategy leveraging the power of topological information graphs. We propose a dynamic adjustment mechanism that iteratively refines the topology of the graph, allowing for adaptive optimization of quantum circuit parameters. We demonstrate the efficacy of this approach through extensive simulations and analyses, showcasing a significant improvement in quantum algorithm performance compared to existing techniques. The core mechanism focuses on exploiting the inherent structure of the graph to guide the optimization process, resulting in enhanced efficiency. This research offers a fundamentally new perspective on algorithm optimization within the quantum realm, holding promise for accelerating the development of practical quantum solutions.

Sep 1, 20260 citations

Quantum Computing Topology Optimization

Jincheng Zhang

This paper introduces a novel quantum computing-based topology optimization algorithm, aiming to address the limitations of traditional optimization methods by leveraging quantum computational principles. We propose a framework that utilizes quantum algorithms to effectively explore the complex topological structures inherent in optimization problems. The core mechanism involves manipulating parameters within a defined topological space, allowing for the discovery of optimal solutions with enhanced efficiency. The paper details the algorithm's design, including the quantum circuit implementation, optimization process, and preliminary results demonstrating its potential for superior performance compared to classical approaches. The research highlights the importance of quantum computing for tackling complex optimization challenges across diverse fields.

Sep 4, 20260 citations

Quantum Graph-Based Parallel Algorithm Optimization

Jincheng Zhang

Quantum Graph-Based Parallel Algorithm Optimization represents a novel approach to algorithm optimization leveraging the unique properties of quantum graphs. This research investigates a framework that transforms algorithms into quantum graphs, exploiting quantum computing's capabilities for enhanced parallel processing and improved performance. The core mechanism focuses on utilizing quantum properties to significantly accelerate algorithm execution. This work demonstrates the potential of quantum graph-based optimization to dramatically enhance algorithm efficiency compared to traditional methods. The study explores the design of a quantum graph representation for various algorithm types, focusing on the impact of quantum properties on parallelization and optimization. The resulting framework offers a promising avenue for advancing algorithm optimization techniques, potentially opening new possibilities for high-performance computing.

Sep 6, 20260 citations

Quantum Computing via Topological Structure Optimization

Jincheng Zhang

Quantum computing holds immense potential to revolutionize various fields, including drug discovery, materials science, and cryptography. However, current quantum algorithms often rely on precise, computationally intensive quantum circuits. This paper explores a novel approach to quantum computation that leverages the unique properties of topological structures to optimize quantum parameter sets, thereby enhancing computational efficiency. We propose a new algorithm based on topological optimization, demonstrating significant improvements over existing methods in specific benchmark problems. This research focuses on the fundamental principles of topological optimization and its application to quantum information processing. The core mechanism involves constructing a topological map representing the parameter space, guiding the search for optimal configurations through a gradient-based optimization process. We provide a detailed description of the algorithm, including the topological map construction, gradient calculation, and optimization procedure. The results showcase the effectiveness of this approach in improving quantum circuit performance and addressing limitations of traditional quantum algorithms.

Sep 6, 20260 citations