Dynamic Quantum Probability Graph

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel dynamic quantum probability graph framework designed to optimize computational performance by automatically adjusting the graph's structure based on the system's state and features. Traditional graph optimization methods often lack the adaptability needed for complex systems. This work proposes a dynamic adjustment mechanism that integrates into the graph structure, enabling efficient and adaptive optimization. We present a theoretical framework and initial implementation demonstrating the framework's effectiveness in a specific quantum simulation scenario. The core mechanism leverages quantum probability maps to dynamically modify graph connectivity and node weights, leading to improved computational efficiency.

DOI: 10.5281/zenodo.22196570

Related papers

Adaptive Quantum Graph Computation

Jincheng Zhang

This paper introduces an adaptive quantum graph computation framework designed to tackle complex graph problems by leveraging the unique properties of quantum graphs. We propose a novel algorithm that dynamically adjusts the quantum circuit parameters based on the graph's structure, enabling efficient and accurate computation and optimization. The core mechanism involves simulating graph behavior, predicting structural and property relationships, and iteratively refining the quantum circuit to achieve optimal results. This approach offers a significant advancement over traditional methods, particularly for large and intricate graphs. The paper details the framework's architecture, the implemented quantum circuit design, and the resulting performance improvements through simulations.

Aug 31, 20260 citations

Dynamic Adaptive Quantum Graph Optimization

Jincheng Zhang

This paper introduces a novel dynamic adaptive quantum graph optimization algorithm designed to enhance the efficiency and performance of quantum computation. Traditional quantum graph optimization methods are often static, limiting the potential for significant improvements. This work proposes a dynamic approach that automatically adjusts the quantum graph structure during the optimization process, leading to more effective solutions and enhanced computational capabilities. The core mechanism centers around iterative adjustments to the graph, guided by a feedback loop that continuously evaluates the quality of the resulting quantum circuits. We demonstrate the effectiveness of this algorithm through a series of simulations and analyses, showcasing its ability to outperform static optimization techniques in various quantum circuit scenarios.

Sep 6, 20260 citations

Quantum Adaptive Graph Structure Optimization

Jincheng Zhang

This paper introduces a novel quantum adaptive graph structure optimization algorithm, aiming to automatically adjust graph structures to optimize problem performance. Traditional graph structure optimization methods rely on manual design, while this approach leverages quantum self-adaptive graph structure optimization algorithms. The core mechanism involves dynamically adjusting graph parameters based on the problem's characteristics, ensuring efficient optimization. This algorithm offers a promising advancement in this field, potentially leading to improved performance across a wide range of problems.

Sep 3, 20260 citations

基于动态量子态的计算图设计

Jincheng Zhang

This paper introduces a novel computational graph design based on dynamic quantum states, aiming to enhance both computational efficiency and precision. Traditional graph-based approaches often rely on static parameter settings, limiting their adaptability to complex calculations. We propose a system where the graph's structure and node relationships dynamically adjust based on quantum state manipulations. This dynamic adjustment leverages the inherent properties of quantum states to optimize computation, offering a significant improvement over static graph approaches. We detail the core mechanism, outlining how quantum state transformations are integrated into the graph structure, and present preliminary experimental results demonstrating enhanced performance in a specific computational task. This work represents a significant step toward more adaptable and efficient computational graph design.

Sep 7, 20260 citations

Dynamic Probability Grids and Quantum Machine Learning

Jincheng Zhang

Abstract: This paper introduces a novel dynamic probability grid framework combined with quantum machine learning algorithms to optimize complex systems. Traditional grid-based optimization methods are static, while the dynamic probability grid offers the ability to automatically adjust and optimize the grid structure. This work demonstrates the effectiveness of this approach in various optimization scenarios, highlighting its potential for automated system design and refinement. The core mechanism involves dynamic grid restructuring guided by quantum machine learning, enabling continuous optimization and adaptation. We present a detailed implementation and analysis of the proposed framework, showcasing its capabilities and potential advantages over existing approaches.

Sep 6, 20260 citations

基于量子自适应的计算图优化

Jincheng Zhang

This paper introduces a novel computational graph optimization technique leveraging quantum self-adaptivity. Traditional graph optimization methods often rely on static parameter settings, which struggle to effectively address the dynamic nature of complex systems. We propose a quantum self-adaptative algorithm that dynamically adjusts quantum state parameters to enhance both computational efficiency and accuracy. The core mechanism involves constructing a dynamic graph structure and employing an iterative adjustment process based on quantum state transformations. This approach offers a significant advancement over existing methods by providing a more flexible and adaptive optimization strategy, particularly suitable for systems exhibiting complex dynamic behavior. The proposed method demonstrates substantial improvements in various graph optimization tasks, highlighting its potential for revolutionizing the field of computational graph design.

Sep 7, 20260 citations