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Dynamic Topological Adaptive Quantum Computation Compilation

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to quantum computation compilation termed "Dynamic Topological Adaptive Quantum Computation Compilation." The core concept revolves around leveraging real-time monitoring of quantum hardware's topological and qubit quality to dynamically optimize circuit compilation. This mitigates the detrimental effects of decoherence and errors inherent in noisy quantum systems. The proposed methodology utilizes machine learning algorithms, specifically reinforcement learning, to analyze the physical characteristics of the quantum hardware and generate optimal circuit execution sequences and gate selections. A key component of this system is the "Quantum Topological Sensor," designed to continuously measure qubit coherence times, connectivity strength, and noise levels. Unlike existing static compilation methods, this dynamic adaptive approach offers real-time adjustment of compilation strategies, addressing the limitations of conventional methods within the challenging environment of current quantum computers. The primary goal is to maximize the fidelity of quantum computations by adapting to the ever-changing conditions of the underlying hardware. The theoretical framework and the proposed algorithm are presented, highlighting the potential for significant improvements in quantum circuit performance.

DOI
10.5281/zenodo.22342027

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This paper proposes a novel approach to quantum computing termed "Dynamic Topological Adaptive Quantum Computing," addressing the limitations of static quantum architectures. The core concept revolves around the ability of a quantum topology to dynamically reshape itself based on input data, maximizing efficiency in quantum algorithm execution. This is achieved by leveraging entangled states within a quantum topological network, coupled with machine learning algorithms. These algorithms enable real-time adjustments to quantum bit connectivity and gate operations, optimizing both circuit pathways and overall efficiency. Unlike existing quantum computing paradigms reliant on fixed topologies, this adaptive system represents a significant advancement, offering the potential for dramatically improved performance and scalability. The theoretical framework presented outlines the key components and operational principles, highlighting the potential for a fundamentally new approach to quantum computation. We explore the mathematical underpinnings of this system, focusing on the control and manipulation of entangled states and the optimization of quantum circuits.