Quantum Entropic Causal Inference

by   Mohammad ali Javidian, et al.

As quantum computing and networking nodes scale-up, important open questions arise on the causal influence of various sub-systems on the total system performance. These questions are related to the tomographic reconstruction of the macroscopic wavefunction and optimizing connectivity of large engineered qubit systems, the reliable broadcasting of information across quantum networks as well as speed-up of classical causal inference algorithms on quantum computers. A direct generalization of the existing causal inference techniques to the quantum domain is not possible due to superposition and entanglement. We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. First, we build the fundamental connection between the celebrated quantum marginal problem and entropic causal inference. Second, inspired by the definition of geometric quantum discord, we fill the gap between classical conditional probabilities and quantum conditional density matrices. These fundamental theoretical advances are exploited to develop a scalable algorithmic approach for quantum entropic causal inference. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links. This successful inference on a synthetic quantum dataset can lay the foundations of identifying originators of malicious activity on future multi-node quantum networks. We unify classical and quantum causal inference in a principled way paving the way for future applications in quantum computing and networking.


page 1

page 2

page 3

page 6

page 7

page 10

page 15

page 23


Quantum Causal Inference in the Presence of Hidden Common Causes: an Entropic Approach

Quantum causality is an emerging field of study which has the potential ...

Causal Inference in Network Economics

Network economics is the study of a rich class of equilibrium problems t...

From Dependence to Causation

Machine learning is the science of discovering statistical dependencies ...

The tropical geometry of causal inference for extremes

Extreme value statistics is the max analogue of classical statistics, wh...

(Causal)-Activation of Complex Entanglement Structures in Quantum Networks

Entanglement represents "the" key resource for several applications of q...

Causal inference for cloud computing

Cloud computing involves complex technical and economical systems and in...

Causal fault localisation in dataflow systems

Dataflow computing was shown to bring significant benefits to multiple n...

Please sign up or login with your details

Forgot password? Click here to reset