Hybrid quantum-classical algorithms for generative models

Quantum machine learning is a field that combines machine learning techniques and quantum computation together. It has the potential of enjoying impressive data analysis power while improving the time efficiency greatly. We propose a new hybrid quantum-classical circuit design for one major problem from machine learning aspect: generative models. We will discuss different ways to construct generative models using quantum algorithms. We will also apply this new design in example datasets and compare the complexity and the results. This work might help to find hidden patterns behind data and offer applications for near-term quantum devices.