Optimized Tree-Structured Entanglement Circuit for Variational Quantum Classification

With the advancement of quantum machine learning, it has demonstrated immense potential in addressing traditionally complex problems. However, existing quantum circuit designs still exhibit limitations in expressibility and model performance. To tackle this challenge, inspired by tensor network structures, this paper proposes an Optimized Tree-Structured Entanglement Circuit (OTSEC). OTSEC employs parameterized quantum gates as fundamental circuit units to construct a hierarchical tree-structured framework for local quantum state initialization and entanglement optimization. Additionally, an Entanglement Adjustment Module (EAM) is introduced to regulate entanglement across different groups of qubits, optimizing long-range correlations and global feature fusion, thereby significantly enhancing the circuit’s expressibility.We further design an Optimized Tree-Structured Variational Quantum Classifier (OT-VQC) and apply it to network intrusion detection tasks. Simulation experiments on the KDD-CUP99 and UNSW-NB15 datasets demonstrate that OT-VQC significantly outperforms conventional variational quantum classifiers with standard circuit structures in key metrics such as classification accuracy, precision, and recall.