Researchers are exploring the use of variational autoencoders to learn complex quantum distributions. Studies have proposed various approaches, including quantum-enabled variational Monte Carlo and quantum Boltzmann-Variational Autoencoder, to improve the efficiency and accuracy of quantum machine learning models. These methods aim to address challenges in fields such as physico-chemical applications and spatiotemporal chaos prediction.