Continuous Variable Quantum MNIST Classifiers

TL;DR

This paper introduces hybrid classical-quantum classifiers using continuous variable quantum neural networks on MNIST, achieving perfect training accuracy with a 4-qumode model on a subset of the dataset.

Contribution

It presents a novel hybrid quantum-classical architecture for multiclass classification using CV quantum neural networks with multiple qumodes.

Findings

Achieved 100% training accuracy with a 4-qumode classifier.

Built eight different classifiers with 2 to 8 qumodes.

Demonstrated the feasibility of CV quantum neural networks for image classification.

Abstract

In this paper, classical and continuous variable (CV) quantum neural network hybrid multiclassifiers are presented using the MNIST dataset. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size equal to n raised to the power of n where n represents cutoff dimension and m, the number of qumodes. They are then translated as one-hot encoded labels, padded with an appropriate number of zeros. The total of eight different classifiers are built using 2,3,...,8 qumodes, based on the binary classifier architecture proposed in Continuous variable quantum neural networks. The displacement gate and the Kerr gate in the CV model allow for the bias addition and nonlinear activation components of classical neural networks to quantum. The classifiers are composed of a classical feedforward 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.