Learning Algebraic Models of Quantum Entanglement
arXiv:1908.10247v20.0011 citations
AI Analysis15
This work addresses quantum state classification, but it appears incremental as it applies existing methods to a new domain.
The paper tackles the problem of classifying quantum entanglement types using supervised learning and deep neural networks, achieving results for up to 5 binary qubits and 3 qutrits.
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).