Summary of generative modeling with quantum hardware
Contents
Summary of generative modeling with quantum hardware¶
In this series of tutorials, we provide a basic introduction into some of the fundamental concepts behind unsupervised generative modelling with quantum hardware. Unsupervised machine learning algorithms have learned breathtaking tasks like the creation of photorealistic images from simple text inputs. Because of this great power, researchers started to translate those concepts to quantum hardware. In this series of tutorials on quantum technologies, we provide a basic introduction into some of the fundamental concepts behind generative modelling with quantum circuits.
Requirements¶
Firstly, we assume that you have the basic notions of quantum computing.
We attempt to make this series self-contained. However, it is highly recommended to be firm with the concepts of beginners guide on quantum machine learning here.
We provide the programming examples in
qiskit.So if you know both things, you can directly start out. Otherwise, the introductory chapters of the qiskit textbook might be a great starting point.
Knowledge of classical supervised learning is not really necessary, but certainly helpful. The Coursera course by deeplearning.ai is awesome.