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Miklin, Nikolai: Characterizing classical and quantum systems from marginal correlationsCharakterisierung von klassischen und Quantensystemen aus Marginaliendaten. 2017
Inhalt
Abstract
Zusammenfassung
Contents
Symbols
1 Introduction
2 Preliminaries
2.1 Entanglement
2.1.1 Bipartite entanglement
2.1.2 Multipartite entanglement
2.1.3 SLOCC classification
2.1.4 Graph and hypergraph states
2.2 Nonlocality
2.2.1 Bell's theorem
2.2.2 Bell inequalities
2.2.3 Correlation polytopes
2.3 Causal models
2.3.1 Directed and undirected graphs
2.3.2 Cause-effect relations
2.3.3 Bayesian networks
2.3.4 Markov random fields
2.3.5 Hidden variables
2.3.6 Causal models in Bell's test
2.3.7 Indefinite causal order
2.4 Marginal problem
2.5 Entropic inequalities
2.5.1 Entropy cone
2.5.2 Probability structures
2.5.3 The entropic characterization of Bayesian networks
2.5.4 The entropic characterization of counterfactuals
2.6 Semidefinite Programming
3 Multiparticle entanglement as an emergent phenomenon
3.1 Emergence of entanglement
3.2 Statement of the problem
3.3 Construction method
3.4 Results
3.4.1 Three qubits
3.4.2 Four and five qubits
3.4.3 Generalization to more particles
3.4.4 Uniqueness of the global state
3.5 Extensions of the problem
3.5.1 Separability of the higher-order marginals
3.5.2 Proving entanglement from a subset of marginals
3.5.3 Higher-dimensional systems
3.5.4 No localizable entanglement in the marginals
3.6 Conclusions
4 Qudit hypergraph states
4.1 Introduction
4.2 Definition of qudit hypergraph states
4.3 SLOCC and LU classes of hypergraphs
4.3.1 Elementary hypergraphs
4.3.2 Tools for SLOCC classification
4.3.3 Tools for LU classification
4.4 SLOCC classification of tripartite hypergraph states in dimensions 3 and 4.
4.4.1 Classification of 333
4.4.2 Classification of 444
4.5 Conclusions
5 Indistinguishability of causal relations from limited marginals
5.0.1 Properties of graphs and hypergraphs
5.1 Adhesivity and independence constraints associated with a marginal scenario
5.1.1 Adhesivity of probabilities
5.1.2 Marginal scenarios admitting a global extension
5.1.3 Maximal set of independence conditions associated with a marginal scenario
5.2 Optimal characterization of the marginal scenario for probabilities and entropies
5.2.1 Triangulation
5.2.2 Probabilities
5.2.3 Entropies
5.2.4 Outer approximations of the entropy cone
5.3 Indistinguishability of causal structures
5.4 Examples and computational results
5.4.1 Inclusions in Obs. 5.4
5.4.2 Three cases in Theorem 5.5
5.5 Conclusions
6 The entropic approach to causal correlations
6.1 Introduction
6.2 Bipartite entropic causal inequalities
6.2.1 Characterization based on causal Bayesian networks
6.2.2 Characterization based on counterfactual variables
6.3 Multipartite entropic causal inequalities
6.3.1 Causal Bayesian network method
6.3.2 Counterfactual variable method
6.4 Information bounds in causal games
6.5 Discussion
Conclusions
Appendix A - An Appendix to Chapter 6
Bibliography
List of publications
Acknowledgments