A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning

no code implementations • 31 Jan 2020 • Jennifer Sleeman, John Dorband, Milton Halem

We formulated an MNIST classification problem using a deep convolutional neural network that used samples from a quantum RBM to train the MNIST classifier and compared the results with an MNIST classifier trained with the original MNIST training data set, as well as an MNIST classifier trained using classical RBM samples.

BIG-bench Machine Learning Image Compression