Operator-Valued Monotone Convolution Semigroups and an Extension of the Bercovici-Pata Bijection

TL;DR

This paper extends the Bercovici-Pata bijection to operator-valued monotone probability, developing a theory of composition semigroups of non-commutative functions and classifying associated Cauchy transforms.

Contribution

It generalizes the Bercovici-Pata bijection to operator-valued monotone probability and develops a comprehensive theory of composition semigroups in this setting.

Findings

Operator-valued monotonically infinitely divisible distributions belong to monotone convolution semigroups.

Extended the classification of Cauchy transforms to more general completely positive maps.

Recaptured classical results on composition semigroups in the operator-valued non-commutative setting.

Abstract

In a 1999 paper, Bercovici and Pata showed that a natural bijection between the classically, free and Boolean infinitely divisible measures held at the level of limit theorems of triangular arrays. This result was extended to include monotone convolution by the authors. In recent years, operator-valued versions of free, Boolean and monotone probability have also been developed. Belinschi, Popa and Vinnikov showed that the Bercovici-Pata bijection holds for the operator-valued versions of free and Boolean probability. In this article, we extend the bijection to include monotone probability theory even in the operator-valued case. To prove this result, we develop the general theory of composition semigroups of non-commutative functions and largely recapture Berkson and Porta's classical results on composition semigroups of complex functions in operator-valued setting. As a biproduct, we deduce that operator-valued monotonically infinitely divisible distributions belong to monotone convolution semigroups. Finally, in the appendix, we extend the result of the second author on the classification of Cauchy transforms for non-commutative distributions to the Cauchy transforms associated to more general completely positive maps.