基于量子纠缠的动态参数调整算法

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper presents a novel dynamic parameter adjustment algorithm based on quantum entanglement, aiming to enhance the precision of quantum computation. Traditional quantum algorithms often rely on static parameter settings, limiting their potential for optimal performance. This research introduces a mechanism that exploits the instantaneous correlations inherent in quantum entanglement to dynamically adjust quantum system parameters, leading to more accurate results. We demonstrate the effectiveness of this approach through a series of theoretical analyses and simulated experiments. The core of the algorithm involves the creation and manipulation of entangled states to achieve near-instantaneous parameter updates, offering a significant advancement in the field of quantum computing. The proposed method addresses limitations of existing approaches by providing a mechanism for adaptive control and potentially unlocking new quantum algorithms. This work outlines the theoretical framework, experimental validation, and implications of this dynamic parameter adjustment technique.

DOI: 10.5281/zenodo.22152556

Related papers

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

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

Dynamic Quantum Entanglement Measurement Algorithm

Jincheng Zhang

This paper introduces a novel dynamic quantum entanglement measurement algorithm designed to enhance measurement precision through adaptive parameter adjustment. The algorithm leverages the unique properties of dynamic quantum entanglement to dynamically adjust measurement parameters during the measurement process. The core mechanism focuses on leveraging the inherent correlations within entangled states to achieve more accurate results compared to traditional measurement techniques. We present a theoretical framework and preliminary experimental validation demonstrating the algorithm's effectiveness in specific scenarios. The paper highlights the potential of this approach to significantly improve measurement fidelity, offering a significant advancement in quantum measurement techniques.

Sep 5, 20260 citations

基于自适应量子态的自适应控制

Jincheng Zhang

This paper introduces a novel algorithm for quantum control based on adaptive quantum states. Traditional quantum control methods often rely on static control parameters, limiting their effectiveness in dynamic environments. The proposed algorithm leverages the unique properties of adaptive quantum states to dynamically adjust quantum states, enabling more efficient quantum control, particularly in areas such as quantum entanglement manipulation and secure key distribution. The core mechanism involves a feedback loop that continuously monitors and adjusts the quantum state to optimize performance. This approach overcomes the limitations of static control strategies, offering a significant improvement in the performance of quantum systems. The algorithm demonstrates improved control precision and robustness through extensive simulations and experimental validation.

Sep 3, 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 Quantum State Optimization

Jincheng Zhang

This paper introduces a novel dynamic quantum state optimization algorithm based on the concept of quantum entanglement, specifically utilizing "quantum pulses" to dynamically adjust quantum state parameters. Traditional quantum state optimization methods are typically static, offering limited adaptability to complex quantum systems. This work proposes a fundamentally new approach that leverages quantum entanglement to achieve self-adaptive optimization, aiming to significantly enhance the performance of quantum computing. The core mechanism involves generating and manipulating entangled states to guide parameter adjustments, thereby enabling a more effective and robust optimization process. We demonstrate the effectiveness of this algorithm through simulations and preliminary analysis, showcasing its potential to overcome limitations of existing optimization techniques.

Sep 2, 20260 citations