التفاصيل البيبلوغرافية
| العنوان: |
Quantum transfer learning for image classification. |
| المؤلفون: |
Subbiah, Geetha, Krishnakumar, Shridevi S., Asthana, Nitin, Balaji, Prasanalakshmi, Vaiyapuri, Thavavel |
| المصدر: |
Telkomnika; Feb2023, Vol. 21 Issue 1, p113-122, 10p |
| مصطلحات موضوعية: |
QUANTUM computing, BLENDED learning, QUANTUM computers, INTEGRATED software, MACHINE learning, CLASSIFICATION |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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