基于自适应的量子图灵机

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel quantum graph-theoretical algorithm, termed the "Adaptive Quantum Graph (AQG)," designed to enhance quantum computation through dynamic qubit arrangement. Traditional quantum algorithms often rely on static circuit designs, limiting their adaptability. The AQG leverages quantum entanglement and quantum gates to dynamically adjust qubit states, enabling a more responsive and efficient computation. The core claim is that this algorithm significantly improves upon existing quantum computation techniques by enabling adaptive qubit manipulation. This advancement is driven by the potential for more flexible and efficient algorithms, particularly in scenarios with variable input data. The paper details the algorithm's core mechanisms, including quantum entanglement and quantum gate manipulation, and provides a conceptual framework for its implementation. This research contributes to the exploration of quantum computing paradigms that move beyond fixed circuit designs toward more adaptive and expressive computation.

DOI: 10.5281/zenodo.22635932

Related papers

基于量子纠缠的自适应计算图

Jincheng Zhang

This paper introduces a novel computational architecture, termed a "Quantum Entangled Adaptive Computation Graph" (QEACG), based on the principles of quantum entanglement and superposition. The QEACG leverages the unique properties of quantum entanglement to dynamically adjust the structure of the computation graph, enabling efficient computation in response to changing input data. We detail the design of the QEACG, outlining its core mechanism involving quantum entanglement, superposition, and a novel graph-based representation. The paper explores the potential benefits of this approach, including enhanced adaptability, improved efficiency, and the capacity to tackle complex problems that are intractable for classical computation. We provide a preliminary analysis of the QEACG's capabilities and discuss future research directions.

Aug 31, 20260 citations

基于自适应的拓扑变换的量子计算框架

Jincheng Zhang

This paper presents a novel quantum computing framework based on adaptive topology transformations. Traditional quantum computing approaches often rely on fixed, static transformations, limiting their expressiveness and computational power. Our framework leverages dynamically adjusted topological transformations, enabling precise and efficient manipulation of quantum states through the application of quantum entanglement. We detail the core mechanism of this framework, emphasizing the dynamic nature of the transformations and their potential to significantly enhance computational capabilities. The proposed method offers a fundamentally new approach to quantum algorithm design and optimization, promising improved performance in complex quantum simulations and quantum state manipulation. We provide a detailed mathematical model and discuss the implications for future research and development in the field of quantum computing.

Sep 7, 20260 citations

基于自适应图神经网络的动态量子算法

Jincheng Zhang

This paper presents a novel dynamic quantum algorithm leveraging self-adaptive graph neural networks (S-GNNs). Traditional quantum algorithms often rely on static parameter settings, limiting their adaptability to complex input data. Our approach introduces a dynamically adjusting S-GNN that automatically adjusts the connection weights and neuron counts, enabling more efficient quantum computation. We demonstrate the algorithm's effectiveness through a series of benchmark tests, highlighting its ability to navigate intricate quantum circuits and achieve improved performance compared to existing methods. The core mechanism centers on a feedback loop that continuously refines the network based on the input, optimizing for a specific quantum circuit's properties. This contributes to a significant advancement in quantum algorithm design and optimization.

Aug 31, 20260 citations

拓扑量子纠缠的非线性自适应计算

Jincheng Zhang

This paper investigates the potential of topological quantum entanglement (TQE) for novel non-linear self-adaptive computation. Traditional quantum algorithms often rely on fixed gate sequences, limiting computational power. We propose a framework centered around dynamic quantum state adaptation, leveraging the unique properties of entangled qubits to achieve significant performance improvements and computational capabilities beyond classical limitations. The core mechanism involves an adaptive quantum state adjuster that responds in real-time to the algorithm's execution, dynamically adjusting the system's quantum state. This approach aims to unlock a new paradigm in quantum computing, enabling algorithms with enhanced efficiency and adaptability. The study analyzes the theoretical foundations and potential practical implementations of this concept, highlighting the challenges and opportunities for future research and development.

Sep 6, 20260 citations

自适应量子态的非线性量子纠缠计算

Jincheng Zhang

This paper introduces a novel framework for quantum state manipulation, specifically focused on the dynamic adjustment of quantum state to enhance computational accuracy. Traditional approaches to quantum state computation often rely on fixed, static parameters, limiting their adaptability to complex input data. We propose a system based on self-adaptive entanglement adjustment, which dynamically adjusts the entanglement between qubits to achieve improved precision in calculations. The core mechanism leverages a feedback loop to optimize the entanglement state, mitigating the limitations of static parameterization. This research explores the potential of this approach to tackle challenges in quantum computation, particularly in scenarios involving dynamic data and intricate entanglement structures. The paper details the theoretical foundation, implementation architecture, and preliminary results demonstrating the effectiveness of this self-adaptive system. The key contribution is a novel algorithm for self-adaptation and a detailed analysis of its impact on computational performance. We believe this framework represents a significant step towards more robust and adaptable quantum computation.

Aug 31, 20260 citations

自适应拓扑结构中的量子纠缠增强计算

Jincheng Zhang

This paper explores the application of adaptive topology in quantum computation to enhance the accuracy and efficiency of quantum entanglement. Traditional quantum algorithms often rely on static parameter settings, limiting their potential. We propose a novel algorithm that dynamically adjusts the topology of a quantum system to optimize entanglement generation and manipulation, effectively boosting computational performance. The core mechanism centers around a self-adaptive topology, allowing the system to evolve and refine its structure in response to the input data, leading to improved results. This research focuses on the fundamental principles of topology and its integration into a quantum computing framework. The paper presents a theoretical framework and preliminary experimental results demonstrating the efficacy of this approach.

Sep 3, 20260 citations