Self-Adaptive Geometric Topology for Quantum Computing Validation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel approach to quantum computing validation based on self-adaptive geometric topology. Traditional validation methods rely on classical verification, which often struggles to capture the subtle nuances of quantum algorithms. We propose a method that dynamically adjusts the quantum system's topology to ensure algorithm correctness and stability. This is achieved through a self-learning algorithm that iteratively refines the system's structure based on its internal characteristics. The core claim is that this approach offers a more efficient and robust validation method, particularly for complex quantum algorithms. This work provides a new framework for validating quantum computations, potentially unlocking significant advancements in quantum algorithm design and implementation.

DOI: 10.5281/zenodo.22184920

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