自适应量子算法的并行计算设计

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum algorithm parallelism, focusing on a dynamically adaptive quantum computing framework. Traditional quantum computing algorithms often rely on static parameter settings, limiting their scalability and performance for complex problems. We propose a mechanism that dynamically adjusts quantum parameters and execution steps to achieve parallel computation efficiency. This design leverages the unique properties of quantum computation to decompose complex problems into manageable sub-tasks, allowing for parallel execution across multiple qubits. The core claim is that this framework offers a significant improvement in performance compared to static parallel algorithms, particularly for problems exhibiting inherent parallelism.

DOI: 10.5281/zenodo.22190568

Related papers

自适应量子算法的并行优化机制

Jincheng Zhang

This paper introduces a novel self-adaptive quantum algorithm optimization mechanism designed to leverage the unique characteristics of quantum hardware and the inherent parallelism of quantum algorithms. The core concept revolves around a dynamic adjustment of parallel execution based on real-time hardware assessments, aiming to significantly enhance the efficiency of quantum computations. We present a framework that analyzes quantum circuit parameters and automatically optimizes the parallelization strategy, leading to improved performance. The paper details the proposed mechanism, its implementation, and preliminary results demonstrating significant improvements over existing approaches, particularly in scenarios involving complex quantum circuits. The focus is on providing a robust and adaptable approach to quantum algorithm optimization.

Sep 6, 20260 citations

自适应量子拓扑的并行计算框架

Jincheng Zhang

Quantum computing holds immense potential, but realizing this potential requires sophisticated algorithms and efficient computational architectures. This paper introduces a novel parallel computing framework based on self-adaptive quantum topology, designed to automatically adjust computational parameters to optimize performance. We propose a mechanism that leverages the inherent properties of quantum topology to dynamically adapt parallel execution strategies, leading to enhanced efficiency compared to traditional approaches. The framework's core concept is the ability to dynamically optimize the parallelism by intelligently adjusting the quantum circuit's parameters based on the system's state. This addresses the limitations of static parallelization methods, offering a potentially transformative approach to quantum computation. We present a theoretical model and preliminary implementation demonstrating the framework's capabilities. The paper concludes with a discussion of future research directions and potential applications of this innovative approach.

Aug 31, 20260 citations

基于自适应的隐冲突的量子计算

Jincheng Zhang

This paper introduces a novel quantum computing framework leveraging adaptive entanglement dynamics to enhance the efficiency of quantum computation. Traditional approaches to entanglement-based quantum algorithms often rely on static parameter settings, limiting scalability and requiring significant computational resources. We propose a dynamic adaptation strategy that intelligently adjusts entanglement parameters based on the input data, leading to a more efficient and robust quantum computation. This framework addresses limitations inherent in existing methods by dynamically optimizing the entanglement landscape, ultimately improving the performance of quantum algorithms. The proposed method demonstrates significant improvements in specific benchmark tasks, showcasing its potential for advancing the field of quantum computing.

Aug 29, 20260 citations

Dynamic Adaptive Quantum Algorithm Framework

Jincheng Zhang

This paper introduces a novel dynamic adaptive quantum algorithm framework designed to optimize quantum computation through dynamic parameter adjustment. Traditional quantum algorithms often rely on fixed parameter settings, limiting their potential for efficient and accurate solutions. This framework leverages quantum computation's inherent properties and data characteristics to dynamically adjust key parameters, resulting in enhanced computational efficiency and accuracy. The core mechanism involves a sophisticated algorithm that continuously analyzes the quantum system and adjusts parameters to achieve optimal performance. This research explores the theoretical underpinnings and practical implementation of this framework, demonstrating its potential to significantly advance quantum algorithm design and performance.

Sep 2, 20260 citations

基于自适应的量子态的超导量子计算算法

Jincheng Zhang

This paper presents a novel approach to quantum computing, leveraging adaptive quantum state parameter adjustment to enhance the stability and efficiency of quantum computations. Traditional quantum algorithms often rely on static parameter settings, limiting their potential for optimization. We propose a dynamic adjustment mechanism that allows the quantum state to evolve in real-time, optimizing quantum operations and mitigating the effects of noise. The core of our algorithm is based on a carefully designed feedback loop that adjusts parameters based on the observed quantum state. This dynamic adaptation significantly improves the resilience of quantum circuits, potentially enabling the realization of complex quantum algorithms. The implementation demonstrates the effectiveness of this approach in a simulated environment, showcasing a marked improvement in coherence and fidelity compared to existing static parameter control methods. The potential implications for quantum computing are significant, offering a pathway towards more robust and scalable quantum systems.

Aug 31, 20260 citations

基于自适应的分布式量子计算算法优化框架

Jincheng Zhang

Distributed quantum computing (DQC) offers immense potential for tackling complex computational problems, but its realization faces significant challenges related to algorithm design, parameter optimization, and scalability. This paper proposes a novel algorithm optimization framework for DQC, centered around a dynamically adaptive quantum state self-adaptation mechanism. The framework leverages the inherent complexity of DQC to automatically adjust quantum parameters, ultimately enhancing the precision and efficiency of computations while enabling support for large-scale quantum systems. We introduce a mechanism to continuously monitor and adjust parameters based on the quantum state, leading to improved performance characteristics. This work addresses the limitations of existing optimization techniques and paves the way for more robust and efficient DQC systems. The core mechanism focuses on a self-tuning approach, dynamically adjusting the quantum state based on the current computation, thereby optimizing performance. This research presents a framework that can dynamically adjust parameters, improving the overall quality of computations.

Sep 3, 20260 citations