Entanglement assisted training algorithm for supervised quantum classifiers

Understand

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.