Tunable Quantum Neural Networks in the QPAC-Learning Framework
Abstract
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.
Cite
@article{arxiv.2205.01514,
title = {Tunable Quantum Neural Networks in the QPAC-Learning Framework},
author = {Viet Pham Ngoc and David Tuckey and Herbert Wiklicky},
journal= {arXiv preprint arXiv:2205.01514},
year = {2023}
}
Comments
In Proceedings QPL 2022, arXiv:2311.08375