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arXiv · 1811.01884

Learning Robust and High-Precision Quantum Controls

Re-Bing Wu·Haijin Ding·Daoyi Dong·Xiaoting Wang
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Abstract

Robust and high-precision quantum control is extremely important but challenging for the functionization of scalable quantum computation. In this paper, we show that this hard problem can be translated to a supervised machine learning task by treating the time-ordered quantum evolution as a layer-ordered neural network (NN). The seeking of robust quantum controls is then equivalent to training a highly {\it generalizable} NN, to which numerous tuning skills matured in machine learning can be transferred. This opens up a door through which a family of robust control algorithms can be developed. We exemplify such potential by introducing the commonly used trick of batch-based optimization, and the resulting stochastic b-GRAPE algorithm is numerically shown to be able to remarkably enhance the control robustness while maintaining high fidelity.

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Re-Bing Wu, Haijin Ding, Daoyi Dong, Xiaoting Wang. 2018-11-05. Learning Robust and High-Precision Quantum Controls. https://doi.org/10.1103/physreva.99.042327

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