A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers

no code implementations • 4 Dec 2019 • Walter Vinci, Lorenzo Buffoni, Hossein Sadeghi, Amir Khoshaman, Evgeny Andriyash, Mohammad H. Amin

The hybrid structure of QVAE allows us to deploy current-generation quantum annealers in QCH generative models to achieve competitive performance on datasets such as MNIST.

PixelVAE++: Improved PixelVAE with Discrete Prior

no code implementations • 26 Aug 2019 • Hossein Sadeghi, Evgeny Andriyash, Walter Vinci, Lorenzo Buffoni, Mohammad H. Amin

Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder.

Image Generation

Quantum-Assisted Genetic Algorithm

no code implementations • 24 Jun 2019 • James King, Masoud Mohseni, William Bernoudy, Alexandre Fréchette, Hossein Sadeghi, Sergei V. Isakov, Hartmut Neven, Mohammad H. Amin

Reverse annealing enables the development of genetic algorithms that use quantum fluctuation for mutations and classical mechanisms for the crossovers -- we refer to these as Quantum-Assisted Genetic Algorithms (QAGAs).

Quantum Variational Autoencoder

no code implementations • 15 Feb 2018 • Amir Khoshaman, Walter Vinci, Brandon Denis, Evgeny Andriyash, Hossein Sadeghi, Mohammad H. Amin

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.