Variational quantum one-class classifier

TL;DR

This paper introduces a variational quantum one-class classifier (VQOCC) that is suitable for noisy quantum computers, offering a compact and effective approach for pattern recognition tasks with performance comparable to classical methods.

Contribution

The paper presents a novel semi-supervised quantum machine learning algorithm that trains a quantum autoencoder without decoding, outperforming some classical models in certain scenarios.

Findings

VQOCC's performance is comparable to OC-SVM and PCA.

Model parameters grow logarithmically with data size.

VQOCC outperforms deep convolutional autoencoders in most cases.

Abstract

One-class classification is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for one-class classification.