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Core Claim: Researching non-linear topological quantum computation using adaptive geometric simplification techniques, automatically adjusting topological structure during computation to reduce computational complexity and improve efficiency.

Jincheng Zhang

Published Sep 6, 2026• 0 citations
Publisher

Abstract

The development of quantum computing has spurred significant research into novel computational paradigms, particularly those leveraging topological quantum systems. Non-linear topological quantum computation offers a promising pathway to enhanced computational power due to its inherent robustness against local noise. This paper investigates a novel approach to improve the efficiency of non-linear topological quantum computation by employing adaptive geometric simplification. We propose a sophisticated algorithm that dynamically adjusts the topology of the underlying quantum system during the computation process, minimizing the number of required steps and ultimately enhancing computational performance. This work addresses a critical challenge in the field – the inherent computational complexity associated with traditional topological quantum algorithms – through the introduction of self-adjusting geometric simplification. We analyze the benefits of this approach in a specific model, demonstrating a significant reduction in computational cost compared to existing methods. The research explores the interplay between topological properties, geometric simplification, and the efficiency of quantum computations. The focus is on developing a practical and scalable algorithm that can be applied to a broader range of non-linear topological quantum systems. The core mechanism involves a self-optimizing geometric simplification algorithm that adjusts the system's topology in real-time, thereby reducing computational bottlenecks.

DOI
10.5281/zenodo.22495193

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