Journal of Quantum Information Science

Volume 12, Issue 2 (June 2022)

ISSN Print: 2162-5751   ISSN Online: 2162-576X

Google-based Impact Factor: 0.95  Citations  

Continuous Variable Quantum MNIST Classifiers
—Classical-Quantum Hybrid Quantum Neural Networks

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DOI: 10.4236/jqis.2022.122005    313 Downloads   1,821 Views  Citations


In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

Share and Cite:

Choe, S. and Perkowski, M. (2022) Continuous Variable Quantum MNIST Classifiers
—Classical-Quantum Hybrid Quantum Neural Networks. Journal of Quantum Information Science, 12, 37-51. doi: 10.4236/jqis.2022.122005.

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