TELKOMNIKA Telecommunication Computing Electronics and Control
Quantum transfer learning for image classification

TELKOMNIKA Telecommunication Computing Electronics and Control
Quantum transfer learning for image classification

Hybrid neural networks
Quantum computing
Transfer learning
Variational quantum circuits

Quantum machine learning, an important element of quantum computing,
recently has gained research attention around the world. In this paper, we have
proposed a quantum machine learning model to classify images using a
quantum classifier. We exhibit the results of a comprehensive quantum
classifier with transfer learning applied to image datasets in particular.
The work uses hybrid transfer learning technique along with the classical
pre-trained network and variational quantum circuits as their final layers on
a small scale of dataset. The implementation is carried out in a quantum
processor of a chosen set of highly informative functions using PennyLane a
cross-platform software package for using quantum computers to evaluate
the high-resolution image classifier. The performance of the model proved to
be more accurate than its counterpart and outperforms all other existing
classical models in terms of time and competence.

Geetha Subbiah, Shridevi S. Krishnakumar, Nitin Asthana , Prasanalakshmi Balaji, Thavavel Vaiyapuri

http://telkomnika.uad.ac.id

Nov 14, 2022

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