基于动态图的符号逻辑的量子计算

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper investigates the potential of dynamic graph-based symbolic logic for enhancing quantum computation, specifically focusing on achieving more powerful logical operations and data processing capabilities. We explore how the transformation of symbolic logic into a dynamic graph framework, coupled with quantum computation's inherent randomness, can lead to significant improvements in computational efficiency and accuracy. This work aims to address key challenges in quantum computing by introducing a novel approach that leverages dynamic graph structures to facilitate complex logical operations and data manipulation. The core mechanism involves dynamically adjusting graph connections, and utilizing quantum randomness to implement sophisticated logical operations, ultimately pushing the boundaries of quantum algorithm design. The paper demonstrates the feasibility of this approach and highlights its potential impact on various quantum computing applications, particularly in artificial intelligence.

DOI: 10.5281/zenodo.22578327

Related papers

基于图的逻辑推理的量子计算

Jincheng Zhang

This paper investigates the potential of leveraging graph-based logic reasoning and quantum computing to tackle complex problems in fields such as drug discovery and cryptography. The core concept is to integrate graph structures with quantum computation to facilitate efficient and robust logical inference. We explore a novel framework that utilizes quantum algorithms to enhance the process of reasoning through complex logical structures represented by graphs. The paper discusses the challenges and opportunities presented by this approach, highlighting its potential to overcome limitations of traditional methods. The investigation delves into the design of quantum algorithms tailored to specific logical inference tasks, considering both the graph representation and quantum computation's inherent advantages. The paper concludes with a preliminary assessment of the feasibility and impact of this integrated approach, suggesting avenues for further research and development.

Aug 31, 20260 citations

基于图的量子态的自适应计算模型

Jincheng Zhang

This paper introduces a novel quantum state computation model based on graph structures, leveraging the inherent properties of quantum states to enable adaptive computation. Traditional quantum computing approaches often struggle with complex quantum states, presenting a significant challenge for efficient computation. Our model proposes a framework that utilizes graph structures to represent quantum states, enabling dynamic modifications to the state's attributes through graph manipulation. This approach offers a potentially transformative path toward enhanced quantum simulation and algorithm development. The core mechanism involves representing quantum states as graphs, where nodes represent states and edges represent relationships between them, allowing for iterative and adaptive computational processes. This research explores the design of such a model and its potential to overcome limitations of existing quantum computation techniques. The paper details the model's architecture, demonstrates its ability to perform specific quantum operations, and discusses future research directions with a focus on scalability and practical applications.

Sep 6, 20260 citations

基于动态拓扑的量子计算的量子拓扑优化

Jincheng Zhang

Quantum computation, leveraging the principles of quantum mechanics, offers the potential for unprecedented computational power. However, the design and optimization of quantum algorithms often involve complex quantum states, demanding sophisticated techniques. This paper introduces a novel approach to quantum computation – a framework based on dynamic topology – that dynamically adjusts quantum state parameters to optimize performance. We explore the application of this technique to quantum topology optimization, demonstrating its potential to enhance the efficiency of quantum algorithms. The core mechanism revolves around leveraging the inherent flexibility of dynamic topology to guide the evolution of quantum states, ultimately leading to improved computational outcomes. This research aims to provide a robust and adaptable method for quantum computation, addressing a critical challenge in the field.

Aug 31, 20260 citations

Based on Dynamic Graphs of Quantum Computation Topology

Jincheng Zhang

Quantum computation, a revolutionary field of science and engineering, has been grappling with the challenge of efficiently simulating complex quantum systems. Traditional approaches often suffer from computational bottlenecks, limiting the size and complexity of the problems that can be tackled. Dynamic graph topology offers a promising alternative, enabling the representation and manipulation of quantum states in a more flexible and expressive manner. This paper investigates the application of dynamic graph topology to quantum computation, focusing on its potential to accelerate the calculation of intricate geometric structures and enhance the overall efficiency of quantum algorithms. We explore the fundamental principles of this approach, detailing the mechanisms behind its utility and outlining the initial results achieved through simulations. The core claim centers on the utilization of dynamic graph topology for optimizing quantum state evolution and achieving more effective geometric calculations. This research provides a novel framework for tackling problems requiring the complex manipulation of quantum states, ultimately contributing to advancements in both quantum algorithm design and computational capabilities.

Aug 31, 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

基于量子纠缠的自适应图谱构建

Jincheng Zhang

This paper explores the potential of leveraging quantum entanglement for adaptive graph construction. Traditional graph representation methods often require manual parameter adjustments, limiting their applicability in dynamic environments. We propose a novel algorithm that utilizes quantum entanglement to dynamically update and adjust graph representations, offering a fundamentally different approach to graph modeling. This research investigates the feasibility of establishing a quantum-enhanced graph structure capable of adapting to evolving data. We demonstrate a preliminary implementation of this algorithm, showcasing its ability to dynamically update graph connectivity based on measured data. This work introduces a new paradigm for graph construction, potentially opening doors to more sophisticated data representation and analysis. The core mechanism centers around exploiting entanglement to facilitate rapid and localized adjustments to the graph's connectivity, providing a method for representing evolving data. The paper concludes with a discussion of future research directions and potential applications of this quantum-inspired graph construction technique.

Aug 31, 20260 citations