8 citations · 9 across the 2 of their papers we have counts for
5 papers
Diversity metric for evaluation of quantum annealing
Alex Zucca, Hossein Sadeghi, Masoud Mohseni +1
Solving discrete NP-hard problems is an important part of scientific discoveries and operations research as well as many commercial applications. A commonly used metric to compare…
A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers
Walter Vinci, Lorenzo Buffoni, Hossein Sadeghi +3
The development of quantum-classical hybrid (QCH) algorithms is critical to achieve state-of-the-art computational models. A QCH variational autoencoder (QVAE) was introduced in Re…
PixelVAE++: Improved PixelVAE with Discrete Prior
Hossein Sadeghi, Evgeny Andriyash, Walter Vinci +2
Constructing powerful generative models for natural images is a challenging task. PixelCNN models capture details and local information in images very well but have limited recepti…
GumBolt: Extending Gumbel trick to Boltzmann priors
Amir H. Khoshaman, Mohammad H. Amin
Boltzmann machines (BMs) are appealing candidates for powerful priors in variational autoencoders (VAEs), as they are capable of capturing nontrivial and multi-modal distributions…
Quantum Variational Autoencoder
Amir Khoshaman, Walter Vinci, Brandon Denis +3
Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE who…