Hybrid quantum-classical classifier based on tensor network and variational quantum circuit
This work addresses the challenge of data dimension reduction for quantum machine learning on NISQ devices, which is a critical bottleneck for researchers developing practical QML applications.
This paper introduces a hybrid quantum-classical classifier that integrates tensor networks (TN) for dimension reduction and variational quantum circuits (VQC) for classification, enabling end-to-end training. They demonstrate that a matrix product state-based TN outperforms PCA as a feature extractor for VQCs in binary classification tasks on the MNIST dataset.
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.