Enhanced Quantum Entanglement Detection of General Two Qubits Systems Based on Modified CNN-BiLSTM Model

Abstract

Entanglement is a key element in quantum information processing. The detection of entanglement is crucial in many long-range quantum information tasks, including secure communication and fundamental tests of quantum physics, but it is also highly resource-intensive. Even for simple 2-qubits systems, satisfactory detection is challenging. In this work, a modified entanglement detection model combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM) is proposed. It shows that the proposed model can effectively extract the deep features and correlations, enabling accurate classification of simple quantum states, even with only a few tens of training samples. When trained with a large number of highly random samples, the model exhibits outstanding fitting capability, resulting in the reliable classification of nearly all common 2-qubits systems. Furthermore, the model exhibits exceptional adaptability and significant application potential in higher-dimensional systems.