论文标题
小量子计算机上高光谱图像的核心
Coreset of Hyperspectral Images on Small Quantum Computer
论文作者
论文摘要
使用机器学习（ML）技术来分析和处理大型遥感（RS）数据，而一种众所周知的ML技术是支持向量机（SVM）。 SVM是二次编程（QP）问题，而D-Wave Quantum Nealelerer（D-WAVE QA）有望比常规计算机更有效地解决此QP问题。但是，D-Wave QA由于其很少的输入量子位而无法直接求解SVM。因此，我们使用给定的EO数据的核心（“数据集的核心”）在此小型D-WAVE QA上训练SVM。核心是原始数据集的一个小的，代表性的加权子集，任何培训模型都通过使用核心来通过使用其原始数据集来生成竞争类别。我们通过采用Kullback-Leibler（KL）差异度量来测量原始数据集及其核心之间的亲密关系。此外，我们通过使用D-Wave QA和常规方法在核心数据上训练了SVM。我们得出的结论是，核心以非常小的KL差异度量来表征原始数据集。此外，我们介绍了KL分歧结果，以证明我们的原始数据与其核心之间的亲密关系。作为实际的RS数据，我们使用美国印度派恩的高光谱图像（HSI）。
Machine Learning (ML) techniques are employed to analyze and process big Remote Sensing (RS) data, and one well-known ML technique is a Support Vector Machine (SVM). An SVM is a quadratic programming (QP) problem, and a D-Wave quantum annealer (D-Wave QA) promises to solve this QP problem more efficiently than a conventional computer. However, the D-Wave QA cannot solve directly the SVM due to its very few input qubits. Hence, we use a coreset ("core of a dataset") of given EO data for training an SVM on this small D-Wave QA. The coreset is a small, representative weighted subset of an original dataset, and any training models generate competitive classes by using the coreset in contrast to by using its original dataset. We measured the closeness between an original dataset and its coreset by employing a Kullback-Leibler (KL) divergence measure. Moreover, we trained the SVM on the coreset data by using both a D-Wave QA and a conventional method. We conclude that the coreset characterizes the original dataset with very small KL divergence measure. In addition, we present our KL divergence results for demonstrating the closeness between our original data and its coreset. As practical RS data, we use Hyperspectral Image (HSI) of Indian Pine, USA.
