Adiabatic quantum computing for deep learning with boltzmann machines

玻尔兹曼机器是机器学习的深度学习方法快速增长的成功的核心，但它们在训练期间需要计算昂贵的蒙特卡洛马尔可夫链（MCMC）。另一方面，D-Wave建立了模拟计算硬件，利用量子隧道效应来有效地执行训练玻尔兹曼机器所需的计算类型。用基于D-wave技术的更高效的硬件计算取代软件MCMC过程，有可能对玻尔兹曼机实际解决的问题规模产生深远影响，并可能对人工智能的研究和应用产生广泛的影响。该项目从实现深度玻尔兹曼机的角度研究了当前D-Wave硬件的局限性，如何绕过这些限制，以及如何利用该硬件的优势来学习传统基于软件的推理具有挑战性的高级表示。

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.