论文标题
纠缠辅助培训算法用于监督量子分类器
Entanglement assisted training algorithm for supervised quantum classifiers
论文作者
论文摘要
我们为监督量子分类器提出了一种新的培训算法。在这里，我们利用了量子纠缠的财产来构建一个模型，该模型可以同时操纵多个培训样本及其标签。随后，构建了基于铃铛的成本函数，可以同时通过任何经典手段来编码多个样本中的错误。我们表明，在最大程度地减少此成本函数后，就可以在基准数据集中成功进行分类。本文提出的结果是针对二进制分类问题。然而，分析也可以扩展到多类分类问题。
We propose a new training algorithm for supervised quantum classifiers. Here, we have harnessed the property of quantum entanglement to build a model that can simultaneously manipulate multiple training samples along with their labels. Subsequently a Bell-inequality based cost function is constructed, that can encode errors from multiple samples, simultaneously, in a way that is not possible by any classical means. We show that upon minimizing this cost function one can achieve successful classification in benchmark datasets. The results presented in this paper are for binary classification problems. Nevertheless, the analysis can be extended to multi-class classification problems as well.
