基于深度学习的量子控制优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum control optimization utilizing deep learning techniques. Traditional quantum control optimization methods often rely on complex analytical calculations or computationally intensive numerical simulations, limiting their applicability to complex quantum systems. This work proposes a framework where a deep learning model is trained to directly learn optimal control pulses for a given quantum system. The model, trained on simulated quantum dynamics, effectively learns the mapping between system states and control parameters. This automated approach offers the potential to significantly accelerate the process of quantum control design and potentially enable control of systems beyond the reach of conventional optimization methods. The core claim of this work is the automation of quantum control through deep learning. The central mechanism involves the construction of a deep learning model to simulate quantum behavior and learn optimal control pulses. The resulting system demonstrates a promising path towards efficient and adaptive quantum control.

DOI: 10.5281/zenodo.22270127

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