# Quantum State Discrimination as Bayesian Experimental Design

## TL;DR

This paper frames quantum state discrimination within Bayesian experimental design, linking utility functions to discrimination strategies and resource monotones, enabling flexible multi-objective tasks and theoretical insights.

## Contribution

It introduces a Bayesian framework for quantum state discrimination, connecting utility functions to discrimination strategies and resource monotones, and extends the theory to multi-objective tasks.

## Key findings

- Discrimination strategies correspond to specific utility functions.
- Success probability and confidence are resource monotones.
- Framework allows mixing and extending discrimination tasks.

## Abstract

We show that quantum state discrimination sits neatly in the framework of Bayesian experimental design. In this setting, the two main branches of quantum state discrimination (minimal error and maximal confidence) simply correspond to two different utility functions. This view allows straightforward extensions and mixing of different discrimination tasks by examining the utility functions, and to describe multi-objective discrimination tasks. In addition, the probability of success and the total confidence are resource monotones quantifying the usefulness of measurements. We give general conditions under which utility functions lead to resource monotones in the resource theory of quantum measurement.