Dynamic Topological Learning for Adaptive Quantum Computing

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the potential of dynamic topological learning (DTL) algorithms to revolutionize quantum computing. The core claim is that quantum computers can leverage DTL to optimize quantum circuit structures, ultimately achieving computational advantages over classical computers. The proposed mechanism utilizes quantum entanglement and superposition to construct a topological representation of the circuit, iteratively refining connections through quantum decoherence and measurement. This adaptive approach overcomes the limitations of fixed-structure quantum algorithms, offering a pathway to dynamic problem adaptation and accelerated solution times. The research focuses on developing a framework for DTL in quantum computing, highlighting the opportunities for enhanced parallel processing and reduced computational complexity. The presented methodology addresses a critical need for adaptable quantum algorithms, paving the way for more efficient and versatile quantum computation. This work establishes a theoretical foundation for DTL and its application within the quantum computing landscape, anticipating future advancements in quantum algorithm design and execution.

DOI: 10.5281/zenodo.22242940

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