Facial Expression Recognition on a Quantum Computer

TL;DR

This paper proposes a quantum machine learning approach for facial expression recognition, utilizing quantum interference and graph-based quantum classifiers, evaluated on IBM's quantum simulator against classical methods.

Contribution

It introduces a novel quantum classifier for facial expressions that leverages graph representations and quantum interference, demonstrating potential advantages over classical classifiers.

Findings

Quantum classifier achieves comparable accuracy to classical methods.

Quantum interference enhances classification efficiency.

Evaluation on IBM Quantum Simulator shows promising results.

Abstract

We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states. We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.