TITLE:
Continuous Variable Quantum MNIST Classifiers—Classical-Quantum Hybrid Quantum Neural Networks
AUTHORS:
Sophie Choe, Marek Perkowski
KEYWORDS:
Quantum Computing, Quantum Machine Learning, Quantum Neural Networks, Continuous Variable Quantum Computing, Photonic Quantum Computing, Classical Quantum Hybrid Model, Quantum MNIST Classification
JOURNAL NAME:
Journal of Quantum Information Science,
Vol.12 No.2,
June
23,
2022
ABSTRACT: 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.