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Dynamic Topological Quantum Circuit Compilation

Jincheng Zhang

Published Sep 5, 2026• 0 citations
Publisher

Abstract

This paper presents a novel approach to topological quantum computation – Dynamic Topological Quantum Circuit Compilation (DTQCC). The core claim is that by continuously monitoring the decoherence performance of a topological quantum circuit, its topology can be dynamically adjusted to achieve enhanced quantum computing efficiency and resilience. This is achieved through a mechanism that establishes a topological decoherence model, utilizes machine learning algorithms to predict decoherence rates, and subsequently, adjusts quantum gate operations and topological qubit connections in real-time to minimize the detrimental effects of decoherence. Unlike previous topological quantum computing methods which largely relied on fixed topologies, the proposed DTQCC framework introduces a dynamic adaptation strategy, significantly improving the adaptability and scalability of topological quantum systems. The core mathematical framework is presented, detailing the equations governing decoherence prediction and topological reconfiguration. We explore the key elements of the system, including the decoherence model formulation, the machine learning algorithm utilized for prediction, and the dynamic reconfiguration strategies. The goal is to demonstrate a pathway towards more robust and efficient topological quantum computation.

DOI
10.5281/zenodo.22315387

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This paper proposes a novel quantum computing architecture centered around dynamic topological adaptation, leveraging real-time monitoring of entanglement strength and coherence times within qubits alongside machine learning algorithms to optimize quantum circuit topology. The core claim is that by continuously assessing qubit characteristics and employing reinforcement learning, the architecture can dynamically adjust the quantum circuit's topology, significantly enhancing computational efficiency and resilience to errors. The proposed system utilizes a distributed quantum processor with individual qubits equipped with sensors to measure their quantum properties. Data from these sensors is transmitted in real-time to a machine learning model, trained using reinforcement learning, which learns to optimize qubit connections and prioritize coherence times. This adaptive approach overcomes the limitations of static quantum circuit design and offers a pathway toward truly self-optimizing quantum computation. The system's ability to respond to the physical state of qubits and adapt the circuit topology based on task requirements promises substantial improvements in quantum computing performance.
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.
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