Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
Abstract
This paper introduces a novel dynamic topology optimization algorithm powered by quantum machine learning. Traditional topology optimization methods often require manual design of the topology, leading to limited flexibility and computational expense. Our algorithm leverages quantum machine learning to automatically learn and optimize the dynamic topology structure of data, resulting in significantly improved optimization efficiency. We propose a framework that utilizes quantum algorithms to represent and manipulate the data distribution, enabling adaptive topology adjustments throughout the optimization process. The core mechanism involves a quantum-enhanced representation of the data's topology, coupled with a reinforcement learning loop to dynamically adjust the topology based on feedback. We demonstrate the effectiveness of this approach through a series of benchmark problems, showcasing enhanced optimization speeds and improved solution quality compared to existing methods. The paper concludes with a discussion of the potential applications of this technology across diverse fields.
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