论文标题
QPAC学习框架中的可调量子神经网络
Tunable Quantum Neural Networks in the QPAC-Learning Framework
论文作者
论文摘要
在本文中，我们研究了量子中可调量子神经网络的性能，这可能是近似正确的（QPAC）学习框架。可调神经网络是由多控制X大门制成的量子电路。通过调整一组控件，这些电路能够近似任何布尔函数。该体系结构特别适合在QPAC学习框架中使用，因为它可以处理甲骨文产生的叠加。为了调整网络，以便它可以近似目标概念，我们已经根据振幅扩增设计并实现了算法。数值结果表明，这种方法可以从简单的类中有效地学习概念。
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.
