Approximation of quantum control correction scheme using deep neural networks
This work addresses the challenge of robust quantum control for researchers in quantum computing, though it appears incremental as it applies an existing neural network method to a specific domain problem.
The paper tackled the problem of correcting quantum control pulses affected by unwanted drift by using LSTM neural networks to model the functional relationship between idealized and drifted pulses, achieving high efficiency in the correction scheme.
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 then analysing the robustness of the latter against local variations in the control profile.