Quantum-Adaptive Nonlinear Matrix Generation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel quantum-adaptive non-linear matrix generation algorithm designed for complex data analysis and simulation. Traditional matrix generation methods often rely on fixed parameters, limiting the versatility and fidelity of the generated matrices. We propose a quantum-based approach that leverages quantum entanglement to create complex matrices with high fidelity. The algorithm dynamically adjusts its parameters to optimize the matrix generation process, offering a significant improvement over existing techniques. We demonstrate the algorithm's effectiveness through theoretical analysis and preliminary simulations, showcasing its potential for advanced data analysis and simulation tasks. The core mechanism focuses on utilizing quantum entanglement to generate matrices with enhanced characteristics, leading to improved accuracy and resilience.

DOI: 10.5281/zenodo.22289433

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