Adiabatic quantum computing for deep learning with boltzmann machines

点击翻译按钮获取中文摘要

Boltzmann machines are at the heart of the rapidly growing success of the deep learning approach to machine learning, but they require computationally expensive Monte-Carlo Markov Chains (MCMC) during training. On the other hand, D-Wave has built analog computing hardware that exploits quantum tunneling effects to efficiently performing the type of computation required to train Boltzmann machines. Replacing the software MCMC process by the more efficient hardware computation based on D-wave's tchnology has the potential to have profound impact on the scale of problems that are practically addressed with Boltzmann machines and could have wide-ranging implications for artificial intelligence research and applications. This project investigates the limitations of the current D-Wave hardware from the point of view of implementing deep Boltzmann machines, how to bypass these limitations, and how to take advantage of the strengths of this hardware to learn higher-level representations for which traditional software-based inference is challenging.