Quantum Information on Spectral Sets

TL;DR

This paper explores the structure of spectral sets in convex optimization and quantum information, highlighting the role of Jordan algebras in defining information-theoretic quantities and measurement reversibility.

Contribution

It characterizes spectral sets as those allowing well-behaved information measures and links them to Jordan algebras, extending quantum information theory beyond Hilbert spaces.

Findings

Spectral sets enable well-defined entropy and divergence.

Jordan algebras generalize quantum information formalism.

Measurement reversibility is tied to spectral set structure.

Abstract

For convex optimization problems Bregman divergences appear as regret functions. Such regret functions can be defined on any convex set but if a sufficiency condition is added the regret function must be proportional to information divergence and the convex set must be spectral. Spectral set are sets where different orthogonal decompositions of a state into pure states have unique mixing coefficients. Only on such spectral sets it is possible to define well behaved information theoretic quantities like entropy and divergence. It is only possible to perform measurements in a reversible way if the state space is spectral. The most important spectral sets can be represented as positive elements of Jordan algebras with trace 1. This means that Jordan algebras provide a natural framework for studying quantum information. We compare information theory on Hilbert spaces with information theory in more general Jordan algebras, and conclude that much of the formalism is unchanged but also identify some important differences.