تقرير
Tunable Quantum Neural Networks in the QPAC-Learning Framework
| العنوان: | Tunable Quantum Neural Networks in the QPAC-Learning Framework |
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| المؤلفون: | Ngoc, Viet Pham, Tuckey, David, Wiklicky, Herbert |
| المصدر: | EPTCS 394, 2023, pp. 221-235 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Quantum Physics |
| مصطلحات موضوعية: | Quantum Physics, Computer Science - Machine Learning |
| الوصف: | In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the set of controls these circuits are able to approximate any Boolean functions. This architecture is particularly suited to be used in the QPAC-learning framework as it can handle the superposition produced by the oracle. In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification. The numerical results show that this approach can efficiently learn concepts from a simple class. Comment: In Proceedings QPL 2022, arXiv:2311.08375 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.4204/EPTCS.394.13 |
| الوصول الحر: | http://arxiv.org/abs/2205.01514Test |
| رقم الانضمام: | edsarx.2205.01514 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.4204/EPTCS.394.13 |
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