Two-qubit causal structures and the geometry of positive qubit-maps
Creators
- 1. Institut für Theoretische Physik, Universität Tübingen, D-72076 Tübingen (Germany)
Description
We study quantum causal inference in a setup proposed by Ried et al (2015 Nat. Phys. 11 414) in which a common cause scenario can be mixed with a cause–effect scenario, and for which it was found that quantum mechanics can bring an advantage in distinguishing the two scenarios: whereas in classical statistics, interventions such as randomized trials are needed, a quantum observational scheme can be enough to detect the causal structure if the common cause results from a maximally entangled state. We analyze this setup in terms of the geometry of unital positive but not completely positive qubit-maps, arising from the mixture of qubit channels and steering maps. We find the range of mixing parameters that can generate given correlations, and prove a quantum advantage in a more general setup, allowing arbitrary unital channels and initial states with fully mixed reduced states. This is achieved by establishing new bounds on signed singular values of sums of matrices. Based on the geometry, we quantify and identify the origin of the quantum advantage depending on the observed correlations, and discuss how additional constraints can lead to a unique solution of the problem. (paper)
Availability note (English)
Available from http://dx.doi.org/10.1088/1367-2630/aad612Additional details
Identifiers
Publishing Information
- Journal Title
- New Journal of Physics
- Journal Volume
- 20
- Journal Issue
- 8
- Journal Page Range
- [19 p.]
- ISSN
- 1367-2630
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52035029
- Subject category
- S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
- Descriptors DEI
- LIMITING VALUES; MAPS; MATRICES; QUANTUM ENTANGLEMENT; QUANTUM MECHANICS; QUBITS; STATISTICS
- Descriptors DEC
- INFORMATION; MATHEMATICS; MECHANICS; QUANTUM INFORMATION