Abstract
Using a diagrammatic reformulation of Bayes' theorem, we provide a necessary and sufficient condition for the existence of Bayesian inference in the setting of finite-dimensional C⁎-algebras. In other words, we prove an analogue of Bayes' theorem in the joint classical and quantum context. Our analogue is justified by recent advances in categorical probability theory, which have provided an abstract formulation of the classical Bayes' theorem. In the process, we further develop non-commutative almost everywhere equivalence and illustrate its important role in non-commutative Bayesian inversion. The construction of such Bayesian inverses, when they exist, involves solving a positive semidefinite matrix completion problem for the Choi matrix. This gives a solution to the open problem of constructing Bayesian inversion for completely positive unital maps acting on density matrices that do not have full support. We illustrate how the procedure works for several examples relevant to quantum information theory.
Original language | English |
---|---|
Pages (from-to) | 28-94 |
Number of pages | 67 |
Journal | Linear Algebra and Its Applications |
Volume | 644 |
DOIs | |
State | Published - Jul 1 2022 |
Funding
We gratefully acknowledge support from the Simons Center for Geometry and Physics, Stony Brook University and for the opportunity to participate in the workshop “Operator Algebras and Quantum Physics” in June 2019. We thank Kenta Cho, Chris Heunen, and Bart Jacobs for sharing their code for the string diagrams used in this paper. We thank Mark Wilde and James Fullwood for comments and suggestions on our first draft. We thank Luca Giorgetti, Alessio Ranallo, and Fidel I. Schaposnik for discussions. Finally, we thank an anonymous reviewer for helpful suggestions. A part of this project was done while AJP was an Assistant Research Professor at the University of Connecticut.
Keywords
- Bayesian inversion
- Choi matrix
- Completely positive
- Positive semidefinite matrix completion
- Quantum information
- Quantum probability