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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