An end-to-end trainable hybrid classical-quantum classifier

• URL: http://arxiv.org/abs/2102.02416v1
• Date: Thu, 4 Feb 2021 05:19:54 GMT
• Title: An end-to-end trainable hybrid classical-quantum classifier
• Authors: Samuel Yen-Chi Chen, Chih-Min Huang, Chia-Wei Hsing and Ying-Jer Kao
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 the availability of the quantum resource by exploiting the correspondence between tensor networks and quantum circuits.

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