Quantum Variational Autoencoder

Understand

Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference.

• Here, we introduce a quantum variational autoencoder (QVAE): a VAE whose latent generative process is implemented as a quantum Boltzmann machine (QBM).
• We show that our model can be trained end-to-end by maximizing a well-defined loss-function: a 'quantum' lower-bound to a variational approximation of the log-likelihood.
• We use quantum Monte Carlo (QMC) simulations to train and evaluate the performance of QVAEs.