Learning Algebraic Models of Quantum Entanglement

Abstract

We review supervised learning and deep neural network design for learning membership on algebraic varieties. We demonstrate that these trained artificial neural networks can predict the entanglement type for quantum states. We give examples for detecting degenerate states, as well as border rank classification for up to 5 binary qubits and 3 qutrits (ternary qubits).

arXiv comment22 pages. comments welcome

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• Activation degree thresholds and expressiveness of polynomial neural networks