Title: Evaluating Quantum Computing Efficiency Through Topological Structure Simulation with Dynamic Graph Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the potential of topological structure simulation as a method for evaluating the efficiency of quantum computation. We propose a novel approach utilizing dynamic graph neural networks (DGNNs) to dynamically adjust network parameters based on system complexity, aiming to enhance the ability to accurately identify and predict complex quantum systems. The core mechanism involves a continuous evaluation process that adjusts the network's parameters, providing a more refined and adaptive assessment of quantum computer performance. The study focuses on demonstrating the effectiveness of this dynamic adjustment in identifying subtle complexities within quantum circuits, ultimately contributing to a more robust and efficient evaluation framework for quantum computing. This work offers a potential shift in the traditional methodology, leveraging advanced neural network techniques to improve the analysis of quantum systems.

DOI: 10.5281/zenodo.22191842

Related papers

Title: Adaptive Quantum Topology Network Analysis

Jincheng Zhang

This paper investigates the application of adaptive quantum topology network analysis for characterizing and optimizing quantum network topologies. Traditional network analysis methods often rely on static models, limiting their ability to capture the dynamic and complex nature of quantum networks. We propose a novel approach that leverages the inherent properties of quantum topology – specifically, its robustness to small perturbations – to provide a more accurate and predictive analysis. The core mechanism involves constructing and analyzing quantum network topologies using a tailored algorithm that dynamically adjusts to the network's evolving state. We demonstrate the effectiveness of this method through simulations, showcasing improved network performance and a more refined understanding of topological constraints. This work represents a significant step towards enhancing the analysis of quantum networks, offering a powerful tool for design and optimization.

Sep 3, 20260 citations

Title: Adaptive Quantum Circuit Dynamic Topology Adjustment

Jincheng Zhang

This paper introduces a novel dynamic topology adjustment mechanism for quantum circuits, leveraging the principles of quantum entanglement and quantum transformation. We propose a system that allows the circuit's topology to be dynamically modified based on input signals, ultimately aiming to enhance circuit efficiency and computational capabilities. The core of this approach involves utilizing quantum entanglement to induce topological changes, guided by a feedback loop that iteratively optimizes the circuit's structure. This results in a more robust and adaptable design compared to traditional static circuit approaches. The study focuses on demonstrating the efficacy of this dynamic topology adjustment in a specific quantum circuit implementation, highlighting its potential for improved performance in various applications. The research investigates the impact of different entanglement parameters and transformation techniques on the circuit's behavior.

Aug 31, 20260 citations

Title: Adaptive Quantum Computation Algorithm for Topological Optimization

Jincheng Zhang

Quantum computation is rapidly advancing, demanding sophisticated algorithms for simulation and optimization. Traditional methods often rely on manual parameter tuning, limiting scalability and efficiency. This paper introduces an adaptive quantum computation algorithm based on topological optimization, leveraging the non-local properties of quantum entanglement to dynamically model quantum states and enhance the accuracy of physical simulations and numerical calculations. We propose a novel framework that automatically adjusts quantum parameters to optimize performance. This approach offers a significant advancement over existing techniques, providing a robust and automated solution for complex quantum computations. The core mechanism centers around exploiting the inherent dynamics of quantum systems through topological structures, enabling adaptive optimization of quantum states. The algorithm's design directly addresses the limitations of traditional quantum computing by automating the optimization process. This work aims to provide a foundation for developing more efficient and scalable quantum algorithms.

Sep 2, 20260 citations

Title: Adaptive Quantum Computing-Based Topology Geometry Modeling

Jincheng Zhang

Adaptive Quantum Computing-Based Topology Geometry Modeling explores the potential of quantum computing to revolutionize geometric modeling. This paper investigates a novel technique that dynamically adjusts geometric parameters during the modeling process, leveraging the unique properties of quantum algorithms. We propose a framework where quantum computations iteratively refine geometric representations, offering a potential improvement over traditional methods by enhancing computational efficiency and enabling the exploration of complex geometric structures. This research focuses on the core mechanism of adapting geometric parameters based on quantum-enhanced optimization, demonstrating its effectiveness through theoretical analysis and preliminary simulation results. The ultimate goal is to develop a robust and scalable method for geometric modeling, significantly advancing the field by integrating quantum principles into a classical computational framework.

Sep 3, 20260 citations

Title: Adaptive Topology-Based Quantum Computing Parameter Adjustment

Jincheng Zhang

Quantum computing offers the potential for unprecedented computational power, but its practical application is hampered by the need for precise control over quantum systems. This paper investigates a novel parameter adjustment framework based on adaptive topology, aiming to enhance quantum computing efficiency through dynamic manipulation of quantum state. We propose a method that dynamically adjusts quantum qubit parameters based on the observed outcome of quantum computations, leveraging the principles of adaptive algorithms to optimize resource utilization. The core mechanism revolves around a self-learning algorithm that continuously refines parameter values to maximize fidelity and minimize error rates, thereby improving the performance of quantum algorithms. This work introduces a new approach to parameter tuning, moving beyond static approaches and embracing a dynamic, adaptive strategy. The paper explores the potential advantages of this framework and presents preliminary results demonstrating its effectiveness in a simulated environment.

Sep 3, 20260 citations

Title: Adaptive Topological Transformation Quantum Computing Model (QATQ)

Jincheng Zhang

This paper introduces a novel quantum computing model, QATQ, based on adaptive topological transformation techniques. We propose a framework that dynamically adjusts the quantum state evolution to simulate system dynamics and leverages topological structures for optimization. The core mechanism focuses on combining quantum algorithms with adaptive topological transformations to improve efficiency and address the challenges of traditional quantum algorithms. This research explores the potential of QATQ to enhance the performance of various quantum computations, offering a fundamentally different approach to quantum system modeling. The paper details the methodology, provides a theoretical analysis, and presents preliminary experimental results demonstrating the efficacy of the QATQ approach. The study aims to advance the state of quantum computing by introducing a dynamic and adaptive method.

Sep 7, 20260 citations