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

Journal Article · · Machine Learning: Science and Technology
ORCiD logo [1];  [2];  [2]; ORCiD logo [3]
  1. Brookhaven National Lab. (BNL), Upton, NY (United States)
  2. National Taiwan Univ., Taipei (Taiwan)
  3. National Taiwan Univ., Taipei (Taiwan); National Center for Theoretical Science, Taipei (Taiwan)
+ Show Author Affiliations

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.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0012704
OSTI ID:
1829278
Alternate ID(s):
OSTI ID: 1835385
Report Number(s):
BNL--222296-2021-JAAM
Journal Information:
Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 4 Vol. 2; ISSN 2632-2153
Publisher:
IOP PublishingCopyright Statement
Country of Publication:
United States
Language:
English

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