Reinforcement learning finds explicit graph realizations for three of six previously unresolved extreme rays of the N=6 holographic entropy cone and supplies evidence that the other three lie outside it.
Superbalance of Holographic Entropy Inequalities
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Defines tripartite complexity and complexity gap for three-subsystem states and reports that the gap has definite sign across holographic CV, Fisher-Rao, and Krylov measures, suggesting it as a building block for complexity inequalities.
Derives conditions for TEE probes, generalizes cyclic and multi-information quantities, and verifies holographic entropy inequalities for gapped topological states.
citing papers explorer
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Exploring the holographic entropy cone via reinforcement learning
Reinforcement learning finds explicit graph realizations for three of six previously unresolved extreme rays of the N=6 holographic entropy cone and supplies evidence that the other three lie outside it.
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Complexity Inequalities for Quantum Subsystems
Defines tripartite complexity and complexity gap for three-subsystem states and reports that the gap has definite sign across holographic CV, Fisher-Rao, and Krylov measures, suggesting it as a building block for complexity inequalities.
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Topological entanglement entropy meets holographic entropy inequalities
Derives conditions for TEE probes, generalizes cyclic and multi-information quantities, and verifies holographic entropy inequalities for gapped topological states.