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2020

Classifying global state preparation via deep reinforcement learning

Haug, Tobias, Mok, Wai-Keong, You, Jia-Bin et al.

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Quantum information processing often requires the preparation of arbitrary quantum states, such as all the states on the Bloch sphere for two-level systems.

While numerical optimization can prepare individual target states, they lack the ability to find general solutions that work for a large class of states in more complicated quantum systems.
Here, we demonstrate global quantum control by preparing a continuous set of states with deep reinforcement learning.
The protocols are represented using neural networks, which automatically groups the protocols into similar types, which could be useful for finding classes of protocols and extracting physical insights.

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