Analysis of Ansätze Expressibility and Complexity and Their Impact on Classification Accuracy Using QNN and QLSTM

Abstract

Quantum Neural Networks (QNN) and Quantum Long Short-Term Memory (QLSTM) models are emerging as powerful tools in quantum machine learning. The effectiveness of these models is largely governed by the structure of their parameterized quantum circuits, also known as Ansätze. The Expressibility (measure of how well an ansätz can explore Hilbert space), Entanglement (which governs the correlation between states of multiple qubits), and Depth (a measure of how many layers of quantum gates a circuit has) are fundamental to a model’s capacity to learn complex patterns. In this paper, we analyze how varying these attributes for various ansätze influences classification cost and accuracy. We evaluated QNN and QLSTM models across three cybersecurity datasets using ten ansätze with varying expressibility and complexity. Results showed that ansätze with lower expressibility and complexity achieved accuracy and training stability comparable to the more complex ones. Additionally, we found that QNN models consistently offered a better trade-off between accuracy, training time, and computational efficiency compared to QLSTM models.