Chrome Extension
WeChat Mini Program
Use on ChatGLM

Characterization of Quantum Betting Tasks in Terms of Arimoto Mutual Information

PRX QUANTUM(2022)

Cited 0|Views0
No score
Abstract
We introduce operational quantum tasks based on betting with risk aversion-or quantum betting tasks for short-inspired by standard quantum state discrimination and classical horse betting with risk aversion and side information. In particular, we introduce the operational tasks of quantum state betting (QSB), noisy quantum state betting (NQSB), and quantum-channel betting (QCB) played by gamblers with different risk tendencies. We prove that the advantage that informative measurements (nonconstant channels) provide in QSB (NQSB) is exactly characterized by Arimoto's alpha-mutual information, with the order alpha determining the risk aversion of the gambler. More generally, we show that Arimoto-type information-theoretic quantities characterize the advantage that resourceful objects offer at playing quantum betting tasks when compared to resourceless objects, for general quantum resource theories (QRTs) of measurements, channels, states, and state-measurement pairs, with arbitrary resources. In limiting cases, we show that QSB (QCB) recovers the known tasks of quantum state (channel) discrimination when alpha -> infinity, and quantum state (channel) exclusion when alpha -> -infinity. Inspired by these connections, we also introduce new quantum Renyi divergences for measurements, and derive a new family of resource monotones for the QRT of measurement informativeness. This family of resource monotones recovers in the same limiting cases as above, the generalized robustness and the weight of informativeness. Altogether, these results establish a broad and continuous family of four-way correspondences between operational tasks, mutual information measures, quantum Renyi divergences, and resource monotones, that can be seen to generalize two limiting correspondences that were recently discovered for the QRT of measurement informativeness.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined