论文标题
具有预计纠缠状态的生成建模
Generative modeling with projected entangled-pair states
论文作者
论文摘要
我们争辩并证明，预测的纠缠态状态（PEPS）的表现要优于矩阵乘积态在具有固有的二维结构（例如图像）的数据集的生成建模方面显着。我们的方法建立在最近引入的用于采样PEPS的算法上，该算法允许对分布进行有效的优化和采样。
We argue and demonstrate that projected entangled-pair states (PEPS) outperform matrix product states significantly for the task of generative modeling of datasets with an intrinsic two-dimensional structure such as images. Our approach builds on a recently introduced algorithm for sampling PEPS, which allows for the efficient optimization and sampling of the distributions.
