An end-to-end trainable hybrid classical-quantum classifier
This work provides an incremental improvement in hybrid quantum-classical machine learning for researchers exploring new classification architectures.
This paper introduces a hybrid classical-quantum classifier that combines a quantum-inspired tensor network and a variational quantum circuit, enabling end-to-end training. The tensor network, specifically a matrix product state with low bond dimensions, was found to be a better feature extractor than principal component analysis for binary and ternary classification on MNIST and Fashion-MNIST datasets.
We introduce a hybrid model combining a quantum-inspired tensor network and a variational quantum circuit to perform supervised learning tasks. This architecture allows for the classical and quantum parts of the model to be trained simultaneously, providing an end-to-end training framework. We show that compared to the principal component analysis, a tensor network based on the matrix product state with low bond dimensions performs better as a feature extractor for the input data of the variational quantum circuit in the binary and ternary classification of MNIST and Fashion-MNIST datasets. The architecture is highly adaptable and the classical-quantum boundary can be adjusted according the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.