Title: Dynamic Lattice-Based Representation of Quantum States

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel dynamic lattice-based representation for quantum states, designed to optimize representation and analysis through adaptive lattice structure adjustment. Traditional lattice methods often rely on fixed parameters, limiting their applicability to specific quantum systems. Our approach dynamically adjusts the connectivity and spacing of the lattice, allowing the representation to adapt to the unique properties of the underlying system. The core mechanism involves analyzing the system's characteristics and modifying the lattice structure accordingly. This adaptive approach significantly enhances the fidelity of quantum state representations, leading to improved analysis and computational efficiency. The design incorporates a mechanism to automatically determine optimal lattice parameters, reducing the need for manual parameter tuning. We demonstrate the effectiveness of this method through simulations and preliminary analysis of specific quantum systems, highlighting its superior performance compared to static lattice representations.

DOI: 10.5281/zenodo.22576550

Related papers

Title: Adaptive Quantum Algorithm Optimization of Quantum Systems

Jincheng Zhang

This paper presents a novel quantum system optimization algorithm based on adaptive quantum system dynamics. Traditional optimization methods often rely on fixed parameters, limiting the potential for improved performance. We propose a dynamically adjustable algorithm that leverages the inherent properties of quantum systems to optimize them in real-time. The core mechanism involves a continuous feedback loop that adjusts algorithm parameters based on the quantum system's state, resulting in enhanced efficiency compared to static optimization techniques. This approach addresses the limitations of current algorithms by providing a framework for adapting to the specific characteristics of each quantum system. The design incorporates a novel weighting scheme to determine the impact of each parameter adjustment, ensuring a balanced and effective optimization process. The results demonstrate significant improvements in various quantum system optimization tasks, particularly in scenarios with complex dynamics.

Sep 3, 20260 citations

Title: Adaptive Quantum Potential Field Simulation

Jincheng Zhang

This paper presents an innovative approach to quantum potential field simulation, leveraging adaptive quantum field parameter adjustment to achieve more accurate and efficient simulations. Traditional methods rely on manual parameter tuning, while this framework utilizes a dynamic adjustment mechanism to automatically optimize the simulation for complex quantum systems. The core of the innovation lies in a self-adjusting system that continuously monitors the system's behavior and modifies the quantum field parameters accordingly, resulting in improved simulation fidelity and enhanced performance in quantum computing. The research demonstrates the effectiveness of this approach through a series of simulations, showcasing significant improvements in the representation of quantum phenomena compared to conventional methods. The paper highlights the benefits of automated parameter adjustment and its potential to accelerate the development of quantum algorithms.

Sep 4, 20260 citations

Title: Adaptive Topology Analysis of Quantum Systems via Quantum Entanglement

Jincheng Zhang

This paper presents a novel algorithm for topology analysis based on quantum entanglement, designed to dynamically adjust the analysis process based on the quantum system's state. Traditional topological analysis methods often rely on static parameter settings, limiting their ability to effectively address complex systems. This research introduces a method that leverages the unique properties of quantum entanglement to provide a truly adaptive approach. The core mechanism involves establishing entangled states between the system and a predefined mapping, allowing the algorithm to iteratively refine the topological structure through entanglement-driven adjustments. This adaptation process leads to improved optimization capabilities compared to conventional methods. We demonstrate the effectiveness of this approach through simulations exploring various complex systems, showcasing its potential for tackling intricate topological configurations.

Sep 1, 20260 citations

Title: Adaptive Quantum Potential Dynamic Computation Framework

Jincheng Zhang

This paper introduces an adaptive quantum potential dynamic computation framework designed to enhance the accuracy and efficiency of quantum simulations. Traditional computational approaches often rely on static parameter adjustments, limiting the framework's flexibility and potentially hindering optimal results. Our framework dynamically adjusts quantum potential parameters based on the specific characteristics of the computational task, allowing for a more tailored and efficient simulation process. We present a novel mechanism integrating the quantum potential concept with the computational framework, enabling this dynamic adaptation. The core claim is that this framework achieves more accurate and efficient simulations compared to traditional methods, particularly in complex systems. This work addresses the limitations of static parameter adjustments in quantum simulation and offers a promising approach for advanced computational modeling.

Aug 31, 20260 citations

Title: Adaptive Quantum Algorithm Parameter Adjustment

Jincheng Zhang

Quantum algorithms, renowned for their potential to solve complex problems beyond classical computation, are notoriously sensitive to initial parameters. Traditional parameter adjustment methods often rely on manual tuning, which can be time-consuming and limited in their ability to adapt to the algorithm's unique characteristics. This paper presents a novel approach to quantum algorithm parameter adjustment utilizing an adaptive mechanism based on the inherent quantum state self-similarity. We propose a system that continuously monitors and adjusts key parameters within a quantum circuit, leveraging the quantum state's dynamic nature to optimize performance and stability. The core mechanism centers around a feedback loop that dynamically adjusts parameters based on the observed evolution of the quantum state. This adaptation allows for more robust and efficient parameter tuning, potentially unlocking significant improvements in algorithm execution speed and accuracy. We investigate the effects of this adaptive approach through theoretical analysis and preliminary experimental validation.

Aug 29, 20260 citations

Title: Adaptive Quantum Computing with Dynamic Topology

Jincheng Zhang

This paper introduces a novel quantum computing algorithm based on dynamic topology, designed for adaptive parameter adjustment. Traditional approaches rely on fixed quantum gate parameters, limiting computational capabilities. Our algorithm leverages the inherent structure of dynamic topology to automatically optimize quantum system parameters, leading to improved precision in quantum computations. This approach addresses the limitations of current quantum algorithms by providing a mechanism for self-tuning and response to system behavior. The core mechanism involves a dynamically evolving topology that adjusts quantum gates based on the system's response, ultimately enhancing the accuracy of results. The paper details the algorithm's design, demonstrates its effectiveness through illustrative examples, and discusses the potential implications for future quantum computing advancements.

Sep 4, 20260 citations