Facial expression recognition on a quantum computer

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 de...

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Bibliographic Details
Published inQuantum machine intelligence Vol. 3; no. 1
Main Authors Mengoni, Riccardo, Incudini, Massimiliano, Di Pierro, Alessandra
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.06.2021
Subjects
Artificial Intelligence
Computational Intelligence
Engineering
Quantum Information Technology
Research Article
Spintronics
Facial expression recognition
Quantum computing
Graph theory
Quantum machine learning
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Summary: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.
ISSN:2524-4906
2524-4914
DOI:10.1007/s42484-020-00035-5