Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

A hybrid quantum-classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum-classical models for other machine learning tasks.

Automated summary

A hybrid quantum-classical approach for modeling continuous classical probability distributions using a variational quantum circuit was proposed. The quantum generator architecture includes encoding classical random variables into quantum states and training a parameterized quantum circuit to mimic the target distribution. The model can interface smoothly with classical functions like neural networks, and is trained using an adversarial learning approach.