Generative Quantum Learning of Joint Probability Distribution Functions

Understand

Modeling joint probability distributions is an important task in a wide variety of fields.

• One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas.
• While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables.
• In this work, we design quantum machine learning algorithms to model copulas.