Liu Jeremy, Yao Ke-Thia, Spedalieri Federico
Department of Computer Science, University of Southern California, Los Angeles, CA 90007, USA.
Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA.
Entropy (Basel). 2020 Oct 24;22(11):1202. doi: 10.3390/e22111202.
Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.
玻尔兹曼机在深度学习应用中发挥着重要作用，例如生成数据建模、为其他类型的网络初始化权重，或从高维数据中提取有效表示。大多数玻尔兹曼机使用受限拓扑结构，排除了循环连接，因为这种连接会产生难以采样的复杂分布。我们使用了一个开放系统量子退火器从复杂分布中采样，并实现具有循环连接的玻尔兹曼机。此外，我们创建了将玻尔兹曼机变量映射到退火器量子比特的策略。这些基于相关性和熵度量的策略在训练期间动态重新配置玻尔兹曼机的拓扑结构并提高性能。
