Yi, B;
Bose, S;
(2022)
Quantum Liang Information Flow as Causation Quantifier.
Physical Review Letters
, 129
(2)
, Article 020501. 10.1103/PhysRevLett.129.020501.
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Abstract
Liang information flow is widely used in classical systems and network theory for causality quantification and has been applied widely, for example, to finance, neuroscience, and climate studies. The key part of the theory is to freeze a node of a network to ascertain its causal influence on other nodes. Such a theory is yet to be applied to quantum network dynamics. Here, we generalize the Liang information flow to the quantum domain with respect to von Neumann entropy and exemplify its usage by applying it to a variety of small quantum networks.
| Type: | Article |
|---|---|
| Title: | Quantum Liang Information Flow as Causation Quantifier |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevLett.129.020501 |
| Publisher version: | https://doi.org/10.1103/PhysRevLett.129.020501 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10153014 |
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