基于动态相干态的超导量子计算框架

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel dynamic-coherent quantum computing framework designed to accelerate quantum learning and adaptation through the dynamic adjustment of quantum states. Traditional quantum computing algorithms often struggle with complex tasks requiring rapid adjustments. This framework leverages a dynamically evolving quantum state, enabling the quantum computer to learn and adapt to changing input parameters more efficiently. The core mechanism centers around the self-adjusting nature of the quantum state, allowing for continuous optimization of quantum properties during computation. We demonstrate the framework's effectiveness through a series of simulations, showcasing improved computational speed and adaptability compared to traditional approaches. The paper concludes with a discussion of the potential impact of this approach on the future of quantum computing.

DOI: 10.5281/zenodo.22299984

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