基于图的逻辑推理的量子计算

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22183699

Related papers

基于图的逻辑推理的量子计算验证

Jincheng Zhang

This paper investigates the potential of utilizing graph-based logical reasoning algorithms to validate quantum computation algorithms, offering a novel approach to quantum algorithm verification that surpasses traditional simulation-based methods. We propose a framework where the core mechanism involves constructing a graph representing the algorithm's state space and applying logical inference to this graph. The goal is to dynamically test the algorithm's behavior and identify potential errors through rigorous logical analysis. The paper details the design of a graph-based verification protocol, explores the benefits of this approach, and discusses preliminary results demonstrating improved efficiency and insight into quantum algorithm robustness. We conclude that this technique represents a significant advancement in quantum algorithm verification, offering a pathway to more reliable and efficient quantum computation.

Sep 3, 20260 citations

基于动态图的符号逻辑的量子计算

Jincheng Zhang

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.

Sep 7, 20260 citations

基于量子计算的符号推理算法

Jincheng Zhang

This paper introduces a novel quantum computing-based symbolic reasoning algorithm designed to enhance the efficiency and accuracy of symbolic reasoning. Traditional symbolic reasoning methods often struggle with computational complexity, particularly when dealing with large and complex knowledge bases. This work proposes a quantum algorithm leveraging quantum bit entanglement and superposition to accelerate the process of logical inference and knowledge retrieval. We explore a specific implementation of this algorithm using a simplified model of logical deduction, demonstrating its potential to outperform classical approaches in certain scenarios. The core mechanism centers on quantum state manipulation to facilitate the exploration of multiple possible reasoning paths concurrently. The paper concludes with a discussion of the challenges and future directions of this research, highlighting the potential for quantum computing to revolutionize symbolic reasoning. The primary goal is to provide a solid theoretical foundation and initial experimental results demonstrating the algorithm's efficacy.

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

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

基于生物拓扑的量子计算算法优化

Jincheng Zhang

Abstract: Quantum computing, a revolutionary paradigm in computation, holds immense potential across diverse fields, including drug discovery, materials science, and cryptography. However, realizing this potential faces significant challenges, primarily due to the inherent complexity of quantum algorithms and their sensitivity to subtle variations. This paper investigates a novel quantum computing algorithm optimization framework based on biological topology, aiming to enhance the efficiency of quantum computations. We introduce a framework leveraging the unique structural properties of biological networks to guide the optimization process, resulting in improved performance compared to traditional methods. The core mechanism centers around identifying and utilizing specific topological patterns within biological data to inform the design of quantum circuits. This approach promises to overcome limitations of existing optimization techniques by introducing a fundamentally different perspective on algorithm design. The study explores the potential of this approach through theoretical analysis and preliminary simulations, demonstrating its effectiveness in optimizing specific quantum circuit designs. This research contributes to the development of robust and adaptable optimization strategies for quantum computing, paving the way for more efficient and scalable quantum systems.

Sep 3, 20260 citations