An end-to-end trainable hybrid classical-quantum classifier
Journal
Machine Learning: Science and Technology
Journal Volume
2
Journal Issue
4
Date Issued
2021
Author(s)
Abstract
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 to the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits. ? 2021 The Author(s).
Subjects
Principal component analysis; Quantum machine learning; Tensor network; Variational quantum circuit; Classification (of information); Machine learning; Network architecture; Quantum theory; Tensors; Timing circuits; Classical-quantum; End to end; Hybrid model; Network-based; Principal-component analysis; Quantum circuit; Quantum machine learning; Tensor network; Training framework; Variational quantum circuit; Principal component analysis
Type
journal article