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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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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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.
AbstractList 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.
ArticleNumber 8
Author Mengoni, Riccardo
Di Pierro, Alessandra
Incudini, Massimiliano
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  organization: Department of Informatics, University of Verona
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Cites_doi 10.1209/0295-5075/119/60002
10.1007/978-3-319-13560-1_17
10.1080/00107514.2014.964942
10.1038/nature23474
10.1109/TCAD.2005.855930
10.1088/1367-2630/17/12/123010
10.5281/zenodo.2562111
10.1007/978-3-662-04245-8_9
10.1007/978-3-540-77974-2
10.1109/CVPRW.2010.5543262
10.1016/j.physleta.2020.126422
10.1098/rspa.2017.0551
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10.1007/978-3-642-31537-4_32
10.1038/s41598-019-40439-3
10.1007/978-1-349-03521-2
10.1103/PhysRevLett.113.130503
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Keywords Facial expression recognition
Quantum computing
Graph theory
Quantum machine learning
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– reference: de Berg M, van Kreveld M, Overmars M, Schwarzkopf OC (2000) Delaunay triangulations. In: Computational geometry: algorithms and applications, pp 183–210. Springer
– reference: CilibertoCHerbsterMIalongoADPontilMRocchettoASeveriniSWossnigLQuantum machine learning: a classical perspectiveProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences2018474220920170551376288710.1098/rspa.2017.05511402.68154
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– reference: WittekPQuantum machine learning: what quantum computing means to data mining2016AmsterdamElsevier Science1349.68006
– reference: Schuld M, Sinayskiy I, Petruccione F (2014) Quantum computing for pattern classification. In: Pham DN, Park SB (eds) Trends in artificial intelligence. PRICAI 2014, Lecture Notes in Computer Science, vol 8862. Springer, Cham
– reference: de BergMCheongOvan KreveldMOvermarsMComputational geometry: algorithms and applications2008BerlinSpringer10.1007/978-3-540-77974-2
– reference: BiamonteJWittekPPancottiNRebentrostPWiebeNLloydSQuantum machine learningNature201754919520210.1038/nature23474
– reference: MottonenMVartiainenJJBergholmVSalomaaMMTransformation of quantum states using uniformly controlled rotationsQuant Inf Comp2005546721675271213.81093
– reference: RebentrostPMohseniMLloydSQuantum support vector machine for big data classificationPhys Rev Lett201411313050310.1103/PhysRevLett.113.130503
– reference: ParkDKPetruccioneFRheeJ-KKCircuit-based quantum random access memory for classical dataScientific Reports201991394910.1038/s41598-019-40439-3
– reference: Bondy JA (1976) Graph theory with applications. Elsevier Science Ltd., GBR
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– reference: NielsenMAChuangILQuantum computation and quantum information, 10th Anniversary2011CambridgeCambridge University Press1288.81001
– reference: Park DK, Blank C, Petruccione F (2020) The theory of the quantum kernel-based binary classifier. Phys Lett A, pp 126422. https://doi.org/10.1016/j.physleta.2020.126422. https://www.sciencedirect.com/science/article/pii/S0375960120302541
– reference: McCleanJRRomeroJBabbushRAspuru-GuzikAThe theory of variational hybrid quantum-classical algorithmsNew J Phys201618202302310.1088/1367-2630/18/2/0230231456.81149
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– reference: SchuldMSinayskiyIPetruccioneFAn introduction to quantum machine learningContemp Phys201556217218510.1080/00107514.2014.9649421342.81091
– reference: Zhao Z, Fitzsimons JK, Rebentrost P, Dunjko V, Fitzsimons JF (2018) Smooth input preparation for quantum and quantum-inspired machine learning. arXiv:1804.00281
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Snippet 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...
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SubjectTerms Artificial Intelligence
Computational Intelligence
Engineering
Quantum Information Technology
Research Article
Spintronics
Title Facial expression recognition on a quantum computer
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