Approximation of quantum control correction scheme using deep neural networks

The main objective and motivation of quantum information processing is the development of new technologies based on principles of quantum mechanics such as superposition and entanglement [1]. Quantum technologies require the development of methods and principles of quantum control, the control theory of the quantum mechanical system [2]. Such methods have to be developed by taking into account the behaviour of quantum systems [3, 4]. In particular, as quantum systems are very susceptible to noise, which may influence the results of the computation, the methods of quantum control have to include the means for counteracting the decoherence [5]. The presented work is focused on the development of tools suitable for analysing the relation between the control pulses used for idealized quantum systems, and the control pulses required to execute the quantum computation in the presence of undesirable dynamics.