Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions

Understand

We propose a hybrid quantum-classical approach to model continuous classical probability distributions using a variational quantum circuit.

• The architecture of the variational circuit consists of two parts: a quantum circuit employed to encode a classical random variable into a quantum state, called the quantum encoder, and a variational circuit whose parameters are optimized to mimic a target probability distribution.
• Samples are generated by measuring the expectation values of a set of operators chosen at the beginning of the calculation.
• Our quantum generator can be complemented with a classical function, such as a neural network, as part of the classical post-processing.