基于量子网络的参数调整算法

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing harnesses the principles of quantum mechanics to perform computations that are intractable for classical computers. However, quantum systems are notoriously sensitive to environmental noise, requiring precise and complex parameter adjustments to achieve optimal performance. Traditional parameter adjustment methods often involve lengthy, iterative processes and can be limited in their ability to effectively explore the parameter space. This paper proposes a novel algorithm based on quantum networks, leveraging quantum entanglement and superposition to dynamically optimize quantum network parameters, ultimately enhancing the efficiency and performance of quantum computations. This research focuses on a systematic approach to parameter adjustment, driven by the unique properties of these quantum phenomena. The core mechanism involves constructing a quantum network and employing quantum algorithms to iteratively adjust parameters based on the network's state evolution. We present a theoretical framework and a preliminary implementation demonstrating the algorithm's potential for improved quantum computation.

DOI: 10.5281/zenodo.22253434

Related papers

基于动态变量的量子态的自适应计算

Jincheng Zhang

This paper investigates a novel approach to quantum computation based on dynamically adjusted qubit states. Traditional quantum algorithms often struggle with the significant environmental noise that limits their performance. We propose a system where the quantum state is continuously perturbed based on internal parameters, dynamically adapting to minimize noise and maximize computational efficiency. This modification leverages the unique properties of quantum entanglement to enable novel computations that are difficult to achieve with traditional methods. The core mechanism involves implementing a feedback loop that constantly adjusts the qubit parameters to achieve the optimal state. We present a theoretical framework and preliminary experimental results demonstrating the potential for enhanced computational performance through this dynamic adjustment strategy. The paper concludes with a discussion of the implications for future quantum algorithm development and the challenges associated with implementing this approach.

Sep 7, 20260 citations

基于拓扑的量子计算参数调整算法

Jincheng Zhang

Quantum computing offers the potential for exponentially faster computation compared to classical computing, driven by the ability to utilize quantum phenomena like superposition and entanglement. However, realizing this potential requires significant computational resources and precise control over quantum states. Parameter adjustment, the process of modifying the parameters of a quantum system to achieve a desired outcome, remains a critical challenge in quantum algorithm design. This paper introduces a novel algorithm, termed "Quantum Topology Optimization" (QTO), that leverages the inherent structure of quantum systems – specifically, topological structures – to dynamically adjust quantum parameters, ultimately leading to optimized computational results. We present a comprehensive theoretical framework and a preliminary implementation demonstrating the effectiveness of QTO in a simplified quantum circuit setting. The core mechanism hinges on automatically mapping and optimizing parameters based on the topological properties of the system, offering a significant departure from traditional parameter adjustment methods. This paper explores the potential of QTO to overcome the limitations of conventional approaches, paving the way for more efficient and robust quantum computation.

Sep 3, 20260 citations

自适应拓扑的量子计算的参数调整算法

Jincheng Zhang

This paper presents a novel algorithm for parameter adjustment in quantum computation, leveraging the principles of adaptive topology. Traditional parameter adjustment methods often rely on fixed algorithms, struggling to effectively handle complex environments. We propose a dynamic adjustment approach that utilizes the self-assembling process of quantum systems, dynamically modifying topological control parameters to optimize quantum computation performance and efficiency. The core mechanism involves a sophisticated optimization process guided by the inherent properties of the quantum system, ultimately leading to enhanced computational capabilities. This research explores the potential of this adaptive approach to overcome the limitations of conventional parameter tuning and advance the field of quantum computing.

Sep 7, 20260 citations

基于量子计算的动态拓扑网络优化

Jincheng Zhang

This paper presents a novel algorithm for dynamic topology optimization, leveraging the unique properties of quantum computing to dynamically adjust network parameters. The core mechanism utilizes quantum entanglement and superposition to efficiently compute and optimize the topology of a network, leading to improved performance compared to traditional optimization techniques. We propose a quantum-enhanced approach to network design, focusing on the dynamic adjustment of node and edge weights to achieve optimal network behavior. This work explores the potential of quantum computation to address challenges in network routing, congestion control, and resource allocation, offering a promising avenue for enhancing network efficiency and resilience. The proposed algorithm demonstrates improved results through extensive simulations and theoretical analysis, highlighting the potential for significant advancements in network management.

Aug 31, 20260 citations

基于自适应的量子计算的拓扑优化算法

Jincheng Zhang

This paper presents a novel quantum computing algorithm for topology optimization, leveraging the inherent properties of quantum computation to dynamically adjust the structure of a given topology. Traditional topology optimization methods often rely on static, pre-defined structures, limiting their adaptability to complex, dynamic environments. Our proposed algorithm utilizes quantum superposition and entanglement to explore multiple solution spaces simultaneously, resulting in improved efficiency and the ability to find more optimal solutions. We demonstrate the effectiveness of this approach through simulations and preliminary experimental results. The core mechanism centers around a quantum-enhanced iterative adjustment of a topological structure, allowing the algorithm to efficiently explore a vast solution space and identify optimal configurations. We highlight the advantages of this approach, including robustness to noise and the potential for discovering novel topological structures.

Sep 6, 20260 citations

自适应拓扑量子计算的量子算法优化

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and the development of efficient quantum algorithms is crucial for realizing this potential. However, the inherent complexity of quantum systems often necessitates sophisticated optimization techniques. This paper introduces a novel approach to quantum algorithm optimization, centered around the dynamic adjustment of quantum algorithm parameters through the exploitation of quantum topology. We propose a framework that dynamically adjusts these parameters based on the system's state, resulting in enhanced algorithm efficiency and accuracy. This method offers a fundamentally new perspective on quantum algorithm optimization, leveraging the unique properties of quantum topology to guide the optimization process. The core mechanism involves manipulating the quantum state to achieve optimal performance. This work presents a preliminary investigation of this approach and outlines potential future research directions.

Sep 4, 20260 citations