Nonlinear Topological Quantum Computation Adaptive Geometry Topology Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents a novel algorithm for optimizing geometric topology in non-linear topological quantum computation. The core idea is to leverage the inherent relationship between a quantum state and its associated topology, allowing for automated adjustment of the topology to enhance computational efficiency. We propose a self-adaptive optimization framework that learns from the interplay of quantum states and topology, offering a potential breakthrough in tackling complex geometric challenges. This work explores the application of topological optimization techniques to quantum computing, addressing a long-standing challenge in the field – achieving efficient quantum computation with complex, dynamically evolving states. The proposed algorithm aims to improve the performance of quantum algorithms by dynamically adjusting the underlying geometric structure.

DOI: 10.5281/zenodo.22495517

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