基于深度学习的量子态预测与优化

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum computing holds immense potential for revolutionizing various fields, but realizing this potential requires overcoming significant challenges related to qubit stability, coherence, and control. Quantum state prediction and optimization are crucial steps in this process. This paper introduces a novel deep learning-based approach for both quantum state prediction and optimization. We leverage the power of deep neural networks to learn the complex patterns within quantum states, enabling accurate predictions of quantum circuit performance and automated optimization of circuit design. The algorithm's effectiveness is demonstrated through theoretical analysis and preliminary experimental validation, showcasing a significant improvement over existing methods. This work represents a significant advancement in quantum algorithm design, offering a promising pathway towards more efficient and reliable quantum computations.

DOI: 10.5281/zenodo.22252266

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