Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

TL;DR

This paper introduces a hybrid quantum-classical classifier combining tensor networks and variational quantum circuits, enabling end-to-end training for improved data compression and classification on NISQ devices.

Contribution

It presents a novel integrated framework using tensor networks and VQCs for supervised learning, outperforming PCA in data compression for quantum classifiers.

Findings

Tensor network-based feature extraction outperforms PCA.

End-to-end training improves classification accuracy.

Framework is adaptable to additional quantum resources.

Abstract

One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.