##基于深度学习的量子态优化控制

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the application of deep learning techniques to the field of quantum state optimization control. Traditional methods for optimizing control pulses for quantum systems are often computationally intensive and may struggle to handle the complexity of realistic quantum systems. This research proposes a novel framework utilizing deep learning to automatically optimize control pulses, aiming for precise control and state evolution of quantum systems. The core of the approach involves constructing a deep learning model where the quantum system state is the input and the control pulse is the output. This model is trained using either reinforcement learning or supervised learning algorithms. The potential of this method to significantly accelerate the optimization process and facilitate control of complex quantum systems is investigated. The resulting optimized control pulses demonstrate improved performance compared to conventional optimization strategies. This work lays the foundation for automated quantum control and opens new possibilities for manipulating quantum systems with greater precision and efficiency.

DOI: 10.5281/zenodo.22338456

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