A Quantum Classifier Based on Tree Structure.

In the era of Noisy Intermediate-Scale Quantum(NISQ), Variational Quantum Algorithm (VQA) is the most promising method to achieve quantum dominance. The VQA is a quantum classical-hybrid algorithm that typically uses a classical optimizer to train a parameterized quantum circuit (PQC). However, the barren plateau phenomenon will occur during training, reducing the training of quantum circuits. In this paper we design a quantum circuit structure, which combines the tree tensor network (TTN) and the parameterized quantum circuit, calling this circuit the tree parameterized quantum circuit (TPQC). We found that the TPQC had better expressiveness and was also effective in alleviating the barren plateau. The quantum classifier of TPQC is used for network attack intrusion detection, and compared with the quantum classifier of universal parameterized quantum circuit, we demonstrate that TPQC has higher accuracy for binary classification tasks.