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arXiv 1808.02516math.OCquant-ph
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基于机器学习的量子李亚普诺夫控制

Quantum Lyapunov control with machine learning

S. C. Hou, X. X. Yi

摘要:

量子态工程是基于李雅普诺夫的量子控制的核心任务。给定不同的初态,如果控制参数,如李雅普诺夫函数,单独优化每个初态,可以实现更好的性能,然而,在计算资源的代价。为了解决这个问题,我们提出了一个初始状态自适应李亚普诺夫控制策略与机器学习,具体来说,人工神经网络训练通过监督学习。提出并说明了两种设计,其中前馈神经网络和一般回归神经网络分别用于选择控制方案和设计李雅普诺夫函数。由于样本的产生和神经网络的训练是预先进行的,初始状态自适应李雅普诺夫控制可以实现没有太多的计算资源增加。

英文摘要:

Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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