学术文献库

The emerging technology of quantum neural networks (QNNs) attracts great attention from both the fields of machine learning and quantum physics with the capability to gain quantum advantage from an artificial neural network (ANN) system. Comparing to the classical counterparts, QNNs have been proven to be able to speed up the information processing, enhance the prediction or classification efficiency as well as offer versatile and experimentally friendly platforms. It is well established that Kerr nonlinearity is an indispensable element in a classical ANN, while, in a QNN, the roles of Kerr nonlinearity are not yet fully understood. In this work, we consider a bosonic QNN and investigate both classical (simulating an XOR gate) and quantum (generating Schrödinger cat states) tasks to demonstrate that the Kerr nonlinearity not only enables non-trivial tasks but also makes the system more robust to errors.