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arXiv · 1904.10573

Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning

Max Wilson·Thomas Vandal·Tad Hogg·Eleanor Rieffel
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Abstract

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum annealer, the D-Wave 2000Q, is used to sample from this model. This algorithm joins a growing family of algorithms that use a quantum annealing subroutine in deep learning, and provides a framework to test the advantages of quantum-assisted learning in GANs. Fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum annealing sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies, and is also applied to the LSUN dataset bedrooms class for the Chimera topology. Evaluated using the Fr\'{e}chet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset.

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Max Wilson, Thomas Vandal, Tad Hogg, Eleanor Rieffel. 2019-04-23. Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning. https://doi.org/10.1007/s42484-021-00047-9

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