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

References in corpus (5)

• Entanglement detection
• Quantum-enhanced machine learning
• Separability Criteria and Entanglement Measures for Pure States of N Identical Fermions
• Building separable approximations for quantum states via neural networks
• A model of interacting quantum neurons with a dynamic synapse

Cited by in corpus (8)

• Neural Projected Quantum Dynamics: a systematic study
• Entanglement classification and \emph{non-k}-separability certification via Greenberger-Horne-Zeilinger-class fidelity
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• 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