Adaptive Geometric Transformations for Quantum Computing Acceleration

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum computing acceleration leveraging adaptive geometric transformations. Traditional quantum algorithms often rely on specific quantum circuits, limiting their potential. We propose a method that dynamically adjusts the geometric properties of qubits to optimize computational performance. This involves utilizing quantum entanglement to accelerate complex calculations and employing self-adaptive geometric transformations to minimize resource consumption. The core mechanism focuses on enhancing the fidelity and speed of quantum computations through this innovative approach, demonstrating significant improvements over existing methods. The paper details the methodology, provides illustrative examples, and discusses the implications for future research in quantum computing.

DOI: 10.5281/zenodo.22265957

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