论文标题
量子机学习的GPU加速近似内核法
GPU-Accelerated Approximate Kernel Method for Quantum Machine Learning
论文作者
论文摘要
基于内核的基于启动势能表面的基于内核的机器学习模型虽然在小型数据制度中准确且方便，但随着训练集尺寸的增加，巨大的计算成本。我们介绍了QML-Lightning，这是一种包含GPU加速近似内核模型的Pytorch软件包，可在几秒钟内减少训练时间，从而在几秒钟内产生训练有素的模型。 QML光线包括FCHL19的经济高效的GPU实施，可以在微秒的时间表上以竞争精度产生能量和力预测。使用现代GPU硬件，我们报告了能量和力量的学习曲线以及时间安排，作为来自QM9，MD-17和3BPA（包括QM9，MD-17和3BPA）的精选基准的数值证据。
Conventional kernel-based machine learning models for ab initio potential energy surfaces, while accurate and convenient in small data regimes, suffer immense computational cost as training set sizes increase. We introduce QML-Lightning, a PyTorch package containing GPU-accelerated approximate kernel models, which reduces the training time by several orders of magnitude, yielding trained models within seconds. QML-Lightning includes a cost-efficient GPU implementation of FCHL19, which together can yield energy and force predictions with competitive accuracy on a microsecond-per-atom timescale. Using modern GPU hardware, we report learning curves of energies and forces as well as timings as numerical evidence for select legacy benchmarks from atomisitic simulation including QM9, MD-17, and 3BPA.
