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Entanglement assisted training algorithm for supervised quantum classifiers
Quantum Information Processing ( IF 2.5 ) Pub Date : 2021-08-02 , DOI: 10.1007/s11128-021-03179-w
Soumik Adhikary 1
Affiliation  

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.



中文翻译:

用于监督量子分类器的纠缠辅助训练算法

我们提出了一种新的监督量子分类器训练算法。在这里,我们利用量子纠缠的特性构建了一个模型,该模型可以同时操纵多个训练样本及其标签。随后,构建了一个基于贝尔不等式的成本函数,它可以以任何经典方法都无法实现的方式同时编码来自多个样本的错误。我们表明,在最小化此成本函数后,可以在基准数据集中实现成功的分类。本文中给出的结果是针对二元分类问题的。尽管如此,该分析也可以扩展到多类分类问题。

更新日期:2021-08-02
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