自适应张量映射 - 动态参数调整的量子计算优化

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel adaptive tensor mapping method designed for quantum computing and beyond. Traditional quantum computing optimization often focuses on algorithm design, while this approach dynamically adjusts tensor mappings to precisely control quantum potential fields, enhancing algorithm efficiency and performance. We propose a self-adaptive algorithm that iteratively refines tensor mappings based on the current system state and target calculation, offering a dynamic and potentially more effective solution than static optimization techniques. The core mechanism involves a feedback loop that continuously adjusts parameters to achieve optimal quantum state. This work aims to address limitations of existing quantum computing optimization strategies by integrating dynamic parameter control and a novel adaptive approach. The paper concludes with a discussion of the potential implications and future research directions of this method.

DOI: 10.5281/zenodo.22192686

Related papers

基于自适应的量子态的自适应映射

Jincheng Zhang

This paper introduces a novel approach to quantum state mapping leveraging adaptive parameter adjustment. Traditional quantum state mapping methods are often static, offering limited flexibility and hindering optimization. We propose a system that dynamically adjusts quantum state parameters based on input data, enabling a more efficient mapping process. The core mechanism involves a feedback loop that continuously refines the quantum state to achieve the desired outcome. This adaptive approach offers a significant improvement over existing techniques, particularly in scenarios requiring complex mappings and dynamic parameter adjustments. The proposed method demonstrates improved performance through rigorous analysis and simulations, highlighting its potential for advancing quantum computing.

Sep 3, 20260 citations

自适应量子势场优化算法

Jincheng Zhang

Quantum computing offers tremendous potential, but realizing this potential requires careful design of quantum systems, particularly their quantum states. Current approaches to quantum state design often rely on static optimization strategies, which struggle to effectively address the inherent complexities of quantum systems. This paper introduces a novel self-adaptive quantum field optimization algorithm, designed to dynamically adjust quantum field parameters to enhance quantum computation performance. We propose a mechanism that continuously refines the quantum field based on the system's dynamics, achieving a more tailored and effective optimization process. The core claim of this research is that this algorithm can significantly improve the performance of quantum computations by optimizing quantum field parameters through a self-adaptive process. The paper details the algorithm's design, validation through simulations, and potential future directions.

Sep 4, 20260 citations

基于自适应的量子势场优化

Jincheng Zhang

The pursuit of accurate and reliable quantum computation necessitates sophisticated control over quantum systems. Traditional approaches often rely on static optimization strategies, limiting the system's ability to dynamically adapt to changing conditions. This paper introduces a novel self-adaptive quantum potential field optimization method, leveraging dynamic adjustment of the quantum potential to achieve precise quantum control and significantly enhance the precision of quantum calculations. The core mechanism centers around an adaptive algorithm that continuously refines the quantum potential based on the system's state, providing a robust and automated approach to quantum control. We present a rigorous theoretical analysis and initial experimental validation demonstrating the superior performance of this method over established static optimization techniques. The focus is on enhancing the accuracy and controllability of quantum systems, opening doors to more complex and impactful quantum computations.

Aug 31, 20260 citations

基于自适应量子势场的自适应计算

Jincheng Zhang

This paper introduces a novel approach to quantum computation leveraging an adaptive quantum field. The core idea is to dynamically adjust the parameters governing a quantum field, allowing for an inherently self-adaptive computation system. We propose a method that employs a feedback loop, iteratively refining the field's characteristics based on observed results and a predefined optimization strategy. This iterative process leads to enhanced computational efficiency and precision compared to static, pre-defined quantum field models. The study focuses on demonstrating the benefits of this adaptive approach through simulations and preliminary experimental validation, highlighting its potential to revolutionize quantum algorithm design. The results emphasize the significant improvements in both computational speed and accuracy attainable through this self-optimizing framework.

Sep 3, 20260 citations

基于自适应自律的量子计算算法优化

Jincheng Zhang

Quantum computing holds immense promise for revolutionizing various fields, and efficient algorithm design is crucial for realizing this potential. However, current quantum algorithms often struggle with optimal parameter settings, limiting their performance. This paper introduces a novel quantum computing algorithm optimization method, leveraging adaptive self-regulation to dynamically adjust quantum computation parameters based on the specific characteristics of the quantum system. We demonstrate the effectiveness of this approach through a series of simulations and analysis, showcasing a significant improvement in algorithm efficiency and precision compared to traditional optimization techniques. The core mechanism focuses on establishing a self-regulating feedback loop that adapts the quantum parameters in real-time, enhancing the algorithm's ability to navigate complex quantum states. This approach offers a more flexible and powerful method for optimizing quantum algorithms, paving the way for enhanced computational capabilities.

Aug 31, 20260 citations

Quantum Adaptive Algorithm Parameter Optimization Strategies

Jincheng Zhang

Quantum adaptive algorithms represent a significant advancement in the field of quantum computing, offering the potential to accelerate complex computations by dynamically adjusting quantum parameters. This paper investigates a novel parameter optimization strategy based on the principles of quantum self-adaptivity, aiming to enhance the performance of existing quantum algorithms. We propose a method that iteratively adjusts quantum state parameters based on feedback from the algorithm's execution, leveraging the inherent capabilities of quantum self-adaptivity to achieve optimal performance. This approach distinguishes itself through a dynamic parameter adjustment cycle that continuously refines the quantum system's state, leading to improved results. The paper details the core mechanism, the algorithm's design, and the experimental validation demonstrating its effectiveness. This work presents a promising path towards more efficient quantum computation.

Sep 6, 20260 citations