Quantum entropic causal inference
APA
Jacob, Z. & Aggarwal, V. (2023). Quantum entropic causal inference. Perimeter Institute. https://pirsa.org/23040119
MLA
Jacob, Zubin, and Vaneet Aggarwal. Quantum entropic causal inference. Perimeter Institute, Apr. 19, 2023, https://pirsa.org/23040119
BibTex
@misc{ pirsa_PIRSA:23040119, doi = {10.48660/23040119}, url = {https://pirsa.org/23040119}, author = {Jacob, Zubin and Aggarwal, Vaneet}, keywords = {Quantum Foundations}, language = {en}, title = {Quantum entropic causal inference}, publisher = {Perimeter Institute}, year = {2023}, month = {apr}, note = {PIRSA:23040119 see, \url{https://pirsa.org}} }

Purdue University

Purdue University
Talk Type
Subject
Abstract
The class of problems in causal inference which seeks to isolate causal correlations solely from observational data even without interventions has come to the forefront of machine learning, neuroscience and social sciences. As new large scale quantum systems go online, it opens interesting questions of whether a quantum framework exists on isolating causal correlations without any interventions on a quantum system. We put forth a theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. At the root of our approach is the proposition that the true causal direction minimizes the entropy of exogenous variables in a nonlocal hidden variable theory. The proposed framework uses a quantum causal structural equation model to build the connection between two fields: entropic causal inference and the quantum marginal problem. First, inspired by the definition of geometric quantum discord, we fill the gap between classical and quantum conditional density matrices to define quantum causal models. Subsequently, using a greedy approach, we develop a scalable algorithm for quantum entropic causal inference unifying classical and quantum causality in a principled way. We apply our proposed algorithm to an experimentally relevant scenario of identifying the subsystem impacted by noise starting from an entangled state. This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multinode quantum networks as well as quantum error correction. As quantum datasets and systems grow in complexity, our framework can play a foundational role in bringing observational causal inference from the classical to the quantum domain.