activity
20182021
most citedQuantum-Assisted Genetic Algorithm

17 citations · 26 across the 3 of their papers we have counts for

collaborators

5 papers

quant-ph20211 cited

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…

quant-ph20198 cited

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…

cs.CV2019

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…

quant-ph201917 cited

Quantum-Assisted Genetic Algorithm

James King, Masoud Mohseni, William Bernoudy +5

Genetic algorithms, which mimic evolutionary processes to solve optimization problems, can be enhanced by using powerful semi-local search algorithms as mutation operators. Here, w…

quant-ph2018

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…