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.

翻译：本文在量子概率近似正确（QPAC）学习框架下研究了可调谐量子神经网络的性能。可调谐神经网络是由多控制X门构成的量子电路，通过调节控制集，这类电路能够逼近任意布尔函数。该架构特别适用于QPAC学习框架，因其可处理预言机产生的量子叠加态。为使网络能够逼近目标概念，我们设计并实现了一种基于振幅放大算法的调谐方法。数值结果表明，该方法能够有效学习简单类别的概念。