论文标题
非负/二进制基质分解的反向退火
Reverse Annealing for Nonnegative/Binary Matrix Factorization
论文作者
论文摘要
最近显示，量子退火可以用作某些类型的矩阵分解算法中的有效，快速的子例程。量子退火算法表现最佳，可快速，近似答案，但性能迅速稳定。在本文中，我们利用反向退火，而不是在非负/二进制矩阵分解问题的量子退火亚例子中进行正向退火。经过正向退火进行最初的全局搜索后，反向退火执行了一系列的本地搜索，以完善现有解决方案。与仅在最短的运行时间以外的所有人相比，向前退火和反向退火的组合显着提高了性能。
It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.
