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Found 6 papers, 0 papers with code
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model
Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum annealer or gate-model device.
RBM-Flow and D-Flow: Invertible Flows with Discrete Energy Base Spaces
We show that D-Flow achieves similar likelihoods and FID/IS scores to those of a typical IF with Gaussian base variables, but with the additional benefit that global features are meaningfully encoded as discrete labels in the latent space.
High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder
Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes.
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.
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 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.
