Approximation of quantum control correction scheme using deep neural networks. (English) Zbl 1504.81091
Summary: We study the functional relationship between quantum control pulses in the idealized case and the pulses in the presence of an unwanted drift. We show that a class of artificial neural networks called LSTM is able to model this functional relationship with high efficiency, and hence the correction scheme required to counterbalance the effect of the drift. Our solution allows studying the mapping from quantum control pulses to system dynamics and analysing its behaviour with respect to the local variations in the control profile.
MSC:
| 81Q93 | Quantum control |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T07 | Artificial neural networks and deep learning |
| 81P73 | Computational stability and error-correcting codes for quantum computation and communication processing |
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