Bayesian generalized probability calculus for density matrices
- 674 Downloads
One of the main concepts in quantum physics is a density matrix, which is a symmetric positive definite matrix of trace one. Finite probability distributions can be seen as a special case when the density matrix is restricted to be diagonal.
We develop a probability calculus based on these more general distributions that includes definitions of joints, conditionals and formulas that relate these, including analogs of the Theorem of Total Probability and various Bayes rules for the calculation of posterior density matrices. The resulting calculus parallels the familiar “conventional” probability calculus and always retains the latter as a special case when all matrices are diagonal. We motivate both the conventional and the generalized Bayes rule with a minimum relative entropy principle, where the Kullbach-Leibler version gives the conventional Bayes rule and Umegaki’s quantum relative entropy the new Bayes rule for density matrices.
Whereas the conventional Bayesian methods maintain uncertainty about which model has the highest data likelihood, the generalization maintains uncertainty about which unit direction has the largest variance. Surprisingly the bounds also generalize: as in the conventional setting we upper bound the negative log likelihood of the data by the negative log likelihood of the MAP estimator.
KeywordsGeneralized probability Probability calculus Density matrix Quantum Bayes rule
- Bhatia, R. (1997). Matrix analysis. Berlin: Springer. Google Scholar
- Feynman, R. P. (1972). Statistical mechanics: a set of lectures. Reading: Addison-Wesley. Google Scholar
- Holevo, A. S. (2001). Lecture notes in physics. Monographs: Vol. 67. Statistical structure of quantum theory, Berlin, New York: Springer. Google Scholar
- Kato, T. (1978). Trotter’s product formula for an arbitrary pair of self-adjoint contraction semigroups. Topics in Functional Analysis (Advances in Mathematics—Supplementary Studies), 3, 185–195. Google Scholar
- Kivinen, J., & Warmuth, M. K. (1999). Averaging expert predictions. In Lecture notes in artificial intelligence : Vol. 1572. Computational learning theory, 4th European conference (EuroCOLT’99), Nordkirchen, Germany, March 29–31, 1999, Proceedings (pp. 153–167). Berlin: Springer. Google Scholar
- Olivares, S., & Paris, M. G. A. Quantum estimation via the minimum Kullback entropy principle. Physical Review A, 76, 2007. Google Scholar
- Singh, R., Warmuth, M. K., Raj, B., & Lamere, P. (2003). Classification with free energy at raised temperatures. In Proc. of EUROSPEECH 2003, September 2003 (pp. 1773–1776) Google Scholar
- Warmuth, M. K. (2005). Bayes rule for density matrices. In Advances in neural information processing systems 18 (NIPS’05). Cambridge: MIT Press. Google Scholar
- Warmuth, M. K. (2007). Winnowing subspaces. In Proceedings of the 24th international conference on machine learning (ICML’07). New York: ACM. Google Scholar
- Warmuth, M. K., & Kuzmin, D. (2006). Online variance minimization. In Proceedings of the 19th annual conference on learning theory (COLT’06), Pittsburg, June 2006. New York: Springer. Google Scholar