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

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