An end-to-end trainable hybrid classical-quantum classifier. Issue 4 (8th September 2021)

• Record Type: Journal Article
• Authors: Chen, Samuel Yen-Chi; Huang, Chih-Min; Hsing, Chia-Wei; Kao, Ying-Jer
• Is Part Of: Machine learning: science and technology. Volume 2:Issue 4(2021)
• Publication Date: 2021-09-08
• Subjects: quantum machine learning -- tensor network -- variational quantum circuit -- principal component analysis; 006.31
• DOI: 10.1088/2632-2153/ac104d
• Languages: English
• ISSNs: 2632-2153

Abstract

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.