Quantum transfer learning for image classification

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


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.


hybrid neural networks; quantum computing; transfer learning; variational quantum circuits;

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DOI: http://doi.org/10.12928/telkomnika.v21i1.24103


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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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