A Lightweight Design for Quantum Networks

Processing data through a quantum classifier is a powerful alternative to traditional machine learning. A quantum classifier consists of a series of parameterized quantum circuits that train known quantum data to approximate classification functions and accurately classify unknown data. Considering the development limitations of quantum computers, this paper proposes a kind of lightweight quantum classifier which contains fewer circuit parameters and integrated with the classical optimization algorithm in order to achieve high-precision classification. The architecture design of quantum classifier is inspired on tensor network states such as matrix product state, tree network (TTN) and multi-scale entangled renormalization (MERA), the classical optimization algorithm adopts gradient descent (GD), particle swarm optimization (PSO) and quantum behavior improved particle swarm optimization (QPSO). The whole classical-quantum hybrid model could theoretically be implemented on near-term devices, and its performance is proved on Anderson Iris data set.