##基于深度学习的量子电路优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores the application of deep learning techniques to optimize quantum circuits. The core challenge in quantum computing lies in the intricate design and manipulation of quantum circuits to achieve desired computational outcomes. Traditional methods of quantum circuit optimization are often computationally expensive and require significant domain expertise. This research proposes a novel approach leveraging deep learning algorithms to automatically generate optimal quantum circuits. The system learns the underlying structure and characteristics of quantum circuits through training, effectively reducing the complexity associated with quantum computation. This work presents a framework for automated quantum circuit optimization, paving the way for more accessible and efficient quantum computing solutions. The key contribution is the integration of deep learning with quantum circuit optimization, offering an automated process for circuit design and potentially unlocking the full potential of quantum computers.

DOI: 10.5281/zenodo.22573655

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