American Geophysical Union Fall Meeting Abstracts

Variational Autoencoders using D-Wave Quantum Annealing

Exploring the use of deep learning algorithms on the quantum computer will provide insight into how the quantum computer, in particular quantum annealing, can be applied to climate related research to accelerate the learning process. Current research has explored using Restricted Boltzmann Machines (RBM) using D-Wave's quantum annealer. This work has explored problems such as MNIST image recognition tasks. In addition, another body of research has explored variational inference methods using quantum annealing. We consider using a combination of the RBM approach and the variational inference approach to implement a deep variational autoencoder to perform latent extractions to support a text-based data assimilation method. We will compare the latent extractions using the quantum variational autoencoder approach with latent extractions produced using a classical variational autoencoder. We will use the D-Wave quantum annealer system and the IBM Power 8 system to perform these experiments. We will compare the effects of the latent extractions on our Dynamic Data Assimilation for Topic Modeling (DDATM) method used to understand how IPCC supported research has evolved over time.

deep learning, quantum computing, topic model


American Geophysical Union


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