Self-Adaptive Quantum Topology Expansion

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive quantum topology expansion algorithm designed to dynamically optimize quantum topology structures in response to system state changes. Traditional quantum topology optimization methods often rely on predefined topologies, hindering their ability to adapt to real-time variations. Our approach leverages machine learning to learn system states and automatically adjust quantum topology, enabling dynamic optimization and continuous monitoring and feedback. We demonstrate the effectiveness of this algorithm through a series of simulations illustrating its adaptability and performance improvements compared to existing methods. The core mechanism involves a learning process that iteratively adjusts the quantum topology based on system feedback. This allows for a flexible and responsive optimization process, offering a significant advancement in the field of quantum topology.

DOI: 10.5281/zenodo.22301830

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