Adaptive Geometry Transformation Optimization based on Quantum Entanglement

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper investigates the application of quantum entanglement to optimize geometric transformations. Traditional optimization techniques often struggle with complex, high-dimensional spaces, and classical methods can be computationally expensive. We propose a novel approach leveraging quantum entanglement to accelerate the optimization process, potentially offering significant improvements in efficiency. The core concept revolves around utilizing entangled qubits to represent and manipulate geometric transformations in a fundamentally different way, allowing for exploration of solution spaces beyond the capabilities of classical algorithms. The proposed method aims to address limitations in computational speed and the capacity of traditional optimization techniques, particularly in scenarios involving intricate geometric transformations. This work will explore the potential of entanglement for accelerating the optimization of geometric transformations, demonstrating its efficacy through theoretical analysis and preliminary experimentation.

DOI: 10.5281/zenodo.22279564

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