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Dynamic Quantum State Field-Based Quantum Computation Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations
Publisher

Abstract

This paper introduces a novel quantum computation algorithm based on dynamic quantum state fields. Traditional quantum algorithms often rely on predefined quantum gates, whereas this approach leverages the dynamic evolution of quantum fields to enable precise control and manipulation of quantum states, facilitating complex quantum simulations. The core of the algorithm involves a continuous simulation of the quantum field, dynamically adjusting parameters to optimize quantum gate operations. We present a preliminary design of this algorithm, detailing the simulation framework and demonstrating its potential for manipulating quantum states with enhanced precision and flexibility. The algorithm's novelty lies in its method of dynamically adjusting the quantum field to achieve precise control and manipulation. This approach offers a significant advancement over existing techniques, particularly in the context of complex quantum simulations.

DOI
10.5281/zenodo.22624257

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Quantum computing holds immense potential for scientific discovery and technological advancement, but the precise control and manipulation of quantum states remain a significant challenge. This paper introduces an adaptive quantum state generation algorithm designed to address this challenge by dynamically adjusting quantum state generation parameters. The algorithm leverages a self-tuning mechanism that continuously optimizes the state generation process based on real-time feedback. This approach offers improved control, reduced error rates, and enhanced optimization capabilities compared to traditional methods. The core of this algorithm involves a feedback loop that analyzes and modifies the parameters affecting the quantum state, ultimately leading to more accurate and reliable quantum state generation. We present a theoretical framework and preliminary experimental results demonstrating the effectiveness of this adaptive approach. This work represents a significant step towards automated and robust quantum state generation, paving the way for more complex quantum computations.
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