基于自适应的概率场模拟的量子计算

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel quantum computing framework based on adaptive probability field simulation. Traditional quantum computation often relies on fixed parameters, limiting the potential for optimization. This work introduces a dynamically adjusted probability field, enabling significant efficiency gains in quantum computation. The framework leverages this adaptive property to accelerate quantum algorithms and explore novel computational pathways. The core mechanism centers around the construction of a "self-adaptive quantum computation" utilizing a dynamically evolving probability field. We analyze the impact of this approach on various quantum algorithms, demonstrating substantial improvements in performance and scalability. This paper proposes a robust and adaptable method for quantum computing, offering a pathway towards more efficient and versatile computation.

DOI: 10.5281/zenodo.22301042

Related papers

基于动态拓扑的量子自适应计算

Jincheng Zhang

This paper introduces a novel quantum self-adaptive computing framework based on dynamic topology. Traditional quantum computation relies on the efficiency of classical computation, but this framework leverages quantum mechanics' inherent adaptability to significantly enhance computational efficiency. The framework utilizes a dynamically evolving topological structure to simulate learning processes, effectively optimizing the quantum algorithm's performance. The core mechanism centers on the dynamic adjustment of topological parameters, mirroring the iterative refinement of a machine learning model. We demonstrate the framework's efficacy through a series of numerical experiments, showcasing improved performance compared to traditional quantum algorithms in specific scenarios. The work presents a fundamentally new approach to quantum self-adaptation, offering a promising pathway towards more efficient and versatile quantum computation.

Aug 29, 20260 citations

基于动态量子算法的概率模型优化

Jincheng Zhang

This paper introduces a novel probabilistic model optimization framework based on dynamic quantum algorithms. Traditional probabilistic model optimization often relies on static parameters, limiting the potential for efficiency gains. Our proposed framework dynamically adjusts quantum state fields, optimizing quantum computation's performance through a continuous, adaptive process. We detail the core mechanism of this framework, outlining the utilization of a quantum algorithm for real-time field manipulation. The resulting optimizations demonstrate a significant improvement in quantum state fidelity and overall computational throughput compared to conventional approaches. This work offers a promising pathway for enhancing the performance of quantum algorithms, particularly those involving complex quantum circuits. The framework's adaptability and the potential for substantial efficiency gains make it a significant advancement in the field of quantum computing.

Aug 31, 20260 citations

基于动态相干态的超导量子计算框架

Jincheng Zhang

This paper presents a novel dynamic-coherent quantum computing framework designed to accelerate quantum learning and adaptation through the dynamic adjustment of quantum states. Traditional quantum computing algorithms often struggle with complex tasks requiring rapid adjustments. This framework leverages a dynamically evolving quantum state, enabling the quantum computer to learn and adapt to changing input parameters more efficiently. The core mechanism centers around the self-adjusting nature of the quantum state, allowing for continuous optimization of quantum properties during computation. We demonstrate the framework's effectiveness through a series of simulations, showcasing improved computational speed and adaptability compared to traditional approaches. The paper concludes with a discussion of the potential impact of this approach on the future of quantum computing.

Sep 4, 20260 citations

基于自适应量子纠缠的自适应计算

Jincheng Zhang

This paper introduces a novel computational framework based on dynamically adjusted quantum entanglement. Traditional quantum computation often relies on static entanglement configurations, while this approach leverages adaptive parameter adjustments to dynamically control and optimize quantum computations. The fundamental principle is to adjust the strength and distribution of entanglement between qubits, thereby influencing the algorithm's behavior and ultimately enhancing its performance. This work explores the design and implementation of a quantum algorithm utilizing this dynamic entanglement control, demonstrating its potential for improved efficiency and robustness. This algorithm aims to overcome limitations associated with fixed entanglement structures, offering a pathway towards more versatile and adaptable quantum computation.

Sep 6, 20260 citations

基于动态概率的模态与量子纠缠的融合

Jincheng Zhang

This paper explores a novel framework for quantum computation and probability computation leveraging dynamic probability. Traditional approaches often struggle to effectively utilize the inherent dynamism of probability in quantum systems. We propose a method that integrates dynamic probability with quantum entanglement, enabling enhanced efficiency and precision in both calculations and probabilistic outcomes. This integration addresses limitations of current quantum computing architectures by providing a more adaptable and robust system. The core mechanism hinges on dynamically adjusting quantum state parameters based on probabilistic feedback, leading to improved performance across a range of quantum algorithms. The resulting framework demonstrates significant potential for accelerating quantum algorithms and improving the reliability of probabilistic outcomes.

Sep 4, 20260 citations

Adaptive Probability Field: A Quantum Computing Optimization Algorithm

Jincheng Zhang

This paper introduces an adaptive probability field optimization algorithm based on quantum computing. Traditional optimization algorithms often rely on fixed parameters, which can limit their effectiveness in complex optimization problems. This work proposes a novel approach utilizing quantum mechanics to dynamically adjust probability field parameters, enabling more efficient and accurate solutions. The core mechanism involves introducing a quantumized probability field and leveraging quantum phenomena like superposition and entanglement to represent and manipulate the parameters in a way that adapts to the specific characteristics of the problem. The algorithm's potential lies in its ability to overcome the limitations of traditional optimization techniques by offering a more flexible and powerful approach to problem-solving. We explore the theoretical foundations and preliminary experimental results demonstrating the effectiveness of this approach, highlighting its advantages and potential applications.

Aug 31, 20260 citations