Abstract: 
Machine learning field has shown incredible impact on many kinds of 
optimization problems. Recently the power of machine learning was 
applied to speed up the quantum states preparation. Although 
approximation with quantum generative adversarial networks is one of the 
fastest ways to prepare a generic quantum state, training time for such 
models is still significant and can easily impair quantum advantage. 
This thesis explores incremental learning of quantum generative 
adversarial networks for the quantum states preparation problem and 
introduces learning use cases reducing the training time.