Entanglement detection with classical deep neural networks

Abstract

In this study, we introduce an autonomous method for addressing the detection and classification of quantum entanglement, a core element of quantum mechanics that has yet to be fully understood. We employ a multi-layer perceptron to effectively identify entanglement in both two- and three-qubit systems. Our technique yields impressive detection results, achieving nearly perfect accuracy for two-qubit systems and over $90\%$ accuracy for three-qubit systems. Additionally, our approach successfully categorizes three-qubit entangled states into distinct groups with a success rate of up to $77\%$. These findings indicate the potential for our method to be applied to larger systems, paving the way for advancements in quantum information processing applications.

12 pages, comments are welcome

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• Entanglement Detection with Quantum-inspired Kernels and SVMs
• A variational quantum algorithm for entanglement quantification
• Effect of Weak Measurement Reversal on Quantum Correlations in a Correlated Amplitude Damping Channel, with a Neural Network Perspective
• Adaptive quantum dynamics with the time-dependent variational Monte Carlo method
• Neural quantum states for entanglement depth certification from randomized Pauli measurements
• Machine Learning of Quantum Entanglement from Noisy Measurements