Self-Adaptive Quantum System Control via Geometric Mapping

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of geometric mapping to self-adaptive quantum system control. Traditional quantum control methods often rely on fixed, classical parameters, presenting limitations in adaptability and precision. We propose a novel algorithm that leverages geometric transformations to dynamically adjust the quantum system's state, offering a significant improvement in control fidelity and optimization capabilities. The core mechanism centers on a self-adaptive control algorithm predicated on geometric transformations, enabling precise state manipulation and optimization. We demonstrate the effectiveness of this approach through simulations, showcasing enhanced control accuracy and improved performance compared to existing methods.

DOI: 10.5281/zenodo.22191319

Related papers

Dynamic Quantum Adaptive Geometry Transformation Guided by Entanglement

Jincheng Zhang

This paper investigates the application of dynamic quantum adaptive geometry transformations guided by entanglement to achieve precise geometric control. Traditional geometric transformations often require iterative refinement, which can be computationally expensive and time-consuming. We propose a novel algorithm that leverages quantum entanglement to dynamically adjust geometric parameters, enabling a significantly reduced number of iterations and improved accuracy. This approach addresses the limitations of conventional methods by offering a fundamentally different approach to geometric manipulation. The core mechanism involves creating and manipulating entangled quantum states to represent the transformation parameters, allowing for precise control and efficient adaptation. We present a theoretical framework and initial implementation demonstrating the algorithm's potential for enhanced geometric precision. The results showcase improved efficiency and accuracy compared to existing methods.

Sep 3, 20260 citations

Geometric Matrix Self-Mapping and Quantum Algorithm Fusion

Jincheng Zhang

This paper presents a novel approach to geometric matrix operations, integrating adaptive self-mapping with quantum algorithms to achieve high-precision geometric matrix transformations and calculations. The core concept revolves around leveraging the inherent self-mapping properties of geometric matrices, coupled with the power of quantum algorithms, to drastically improve computational efficiency. We introduce a novel self-mapping algorithm that systematically adjusts the matrix's transformation based on the input, enabling a more robust and efficient transformation process. The integration of quantum algorithms enhances the accuracy and speed of these transformations, offering a significant advancement over existing methods. This work addresses the limitations of traditional geometric matrix techniques and demonstrates the potential for a synergistic approach to enhance computational capabilities. The algorithm's design incorporates a framework for adaptive self-mapping, allowing for dynamic adjustment based on the input data, thereby improving the overall performance. We provide a detailed theoretical analysis, discuss implementation considerations, and present preliminary experimental results showcasing the algorithm's efficacy.

Sep 4, 20260 citations

Quantum State Adaptive Geometry Mapping

Jincheng Zhang

Quantum state adaptive geometry mapping is a novel approach to control and optimization of quantum systems by leveraging the inherent nonlinear properties of quantum states. This paper introduces a new algorithm that utilizes these properties to precisely control and map the internal geometry of a quantum system, offering a fundamentally different approach compared to traditional geometry mapping techniques. We demonstrate the effectiveness of this method through simulations, showcasing its ability to achieve precise state manipulation and topology changes. The core mechanism centers around a dynamically adjusted mapping function that responds to the quantum state's dynamics, ensuring accurate and efficient control.

Aug 31, 20260 citations

Quantum State Optimization through Non-Linear Geometry

Jincheng Zhang

Quantum state optimization is a critical challenge in the development of quantum devices, requiring precise control over quantum states to maximize performance. Traditional optimization methods often struggle with complex, non-linear geometry, limiting the potential for significant improvements. This paper proposes a self-optimizing algorithm that leverages non-linear geometry to dynamically adjust quantum state geometry, enhancing device performance. We demonstrate the effectiveness of this approach through a novel simulation and analysis of a quantum system, highlighting its ability to achieve superior results compared to existing optimization techniques. The core mechanism centers around constructing a geometric structure that responds directly to data, enabling adaptive optimization. This work presents a fundamentally new approach to quantum state optimization, potentially unlocking new levels of device capability.

Sep 3, 20260 citations

The Adaptive Geometry Transformation Quantum Computing Simulation Algorithm

Jincheng Zhang

This paper introduces an adaptive geometry transformation quantum computing simulation algorithm designed to enhance the accuracy and efficiency of complex system simulations. Traditional quantum computing methods often rely on manual design, limiting the potential for improved simulation fidelity. Our algorithm leverages quantum entanglement and geometric transformations to dynamically adjust the simulation parameters, leading to more precise and robust numerical results. We demonstrate the effectiveness of this approach through a focused simulation of a quantum system exhibiting complex dynamic behavior. The core mechanism involves a novel method for generating and manipulating entangled states, which is then used to iteratively refine the simulation's parameters to achieve optimal accuracy. The algorithm's ability to self-adapt to the system's dynamics represents a significant advancement in quantum computing simulation.

Sep 1, 20260 citations

Quantum Geometry Adaptive Algorithm

Jincheng Zhang

This paper introduces a novel quantum geometry-based adaptive algorithm designed to precisely calculate and optimize geometric structures. Traditional optimization methods often rely on manual design, limiting efficiency. Our algorithm leverages the unique properties of quantum geometry to dynamically adjust its parameters, enabling accurate solutions across a wide range of geometric problems. This adaptive approach contrasts with conventional algorithms, offering significant improvements in speed and accuracy. The core mechanism centers on the algorithm's ability to map geometric transformations and constraints to quantum states, facilitating a form of "quantum intuition" in the optimization process. This paper details the algorithm's architecture, introduces key mathematical concepts, and presents preliminary results demonstrating its effectiveness in a series of challenging geometric optimization tasks.

Sep 2, 20260 citations