Machine-Learning-Enhanced Entanglement Detection Under Noisy Quantum Measurements

Quantum measurements are inherently noisy, hindering reliable entanglement detection and limiting the scalability of quantum technologies. While error mitigation and correction strategies exist, they often impose prohibitive resource overheads. Here, we introduce a machine-learning-based approach to achieve noise-resilient entanglement classification even with imperfect measurements. Using support vector machines (SVMs) trained on features extracted from Pauli measurements, we develop a robust optimal entanglement witness (ROEW) that remains effective under unknown measurement noise. By optimizing SVM parameters against worst-case errors, our protocol significantly outperforms conventional methods in classification accuracy. Numerical experiments demonstrate that ROEW achieves high-fidelity entanglement detection with minimal measurements, even when measurement errors exceed 10\%. This work bridges machine learning and quantum information science, offering a practical tool for noise-robust quantum characterization and advancing the feasibility of entanglement-based technologies in real-world settings.