Search Results for author:
Found 7 papers, 1 papers with code
A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealers
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.
Scaling advantage in quantum simulation of geometrically frustrated magnets
By initializing the system in a state with topological obstruction, we observe quantum annealing (QA) relaxation timescales in excess of one microsecond.
Quantum Physics Statistical Mechanics Emerging Technologies
PixelVAE++: Improved PixelVAE with Discrete Prior
Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder.
Ranked #22 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Quantum-Assisted Genetic Algorithm
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).
GumBolt: Extending Gumbel trick to Boltzmann priors
The Gumbel trick resolves this problem in a consistent way by relaxing the variables and distributions, but it is incompatible with BM priors.
Quantum Variational Autoencoder
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.
Quantum Boltzmann Machine
Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian.
Quantum Physics
