##基于深度学习的量子门优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores the application of deep learning techniques to the optimization of quantum gates, a critical step towards enhancing the efficiency of quantum computing. The core idea is to leverage the pattern recognition capabilities of deep neural networks to automate the design process of quantum gates, moving beyond traditional, often manual, optimization methods. We propose a deep learning-based model that learns from experimental data related to quantum computation to iteratively refine gate designs. The model's architecture is designed to minimize gate fidelity and maximize circuit performance. This research represents a novel approach to quantum gate optimization, offering the potential for automated and data-driven gate design, ultimately contributing to the scalability and practicality of quantum computing. The key contributions of this work include the development of a trainable deep learning model for quantum gate optimization and the demonstration of its potential to significantly improve gate performance. ---

DOI: 10.5281/zenodo.22474228

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