Abstracct

Bayes' rule, which is routinely used to update beliefs based on new evidence, can be derived from a principle of minimum change. This principle states that updated beliefs must be consistent with new data, while deviating minimally from the prior belief. Here, we introduce a quantum analog of the minimum change principle and use it to derive a quantum Bayes' rule by minimizing the change between two quantum input-output processes, not just their marginals. This is analogous to the classical case, where Bayes' rule is obtained by minimizing several distances between the joint input-output distributions. When the change maximizes the fidelity, the quantum minimum change principle has a unique solution, and the resulting quantum Bayes' rule recovers the Petz transpose map in many cases.

About the speaker

Ge Bai is an Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou). His research focuses on quantum causal inference, quantum machine learning and quantum communication network theory. Before his current position, he was a postdoctoral fellow at the National University of Singapore. He received his PhD from the University of Hong Kong, where he was awarded the Hong Kong Young Scientist Award.