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February 26, 2026Quantum Information Processing0 citationsOpen Access

Computation of Smooth Max-Mutual Information via Semidefinite Programming

Computation of the smooth max-mutual information via semidefinite programming

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Authors

CPChristopher PoppTSTobias C. SutterBHBeatrix C. Hiesmayr

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Overview

An iterative algorithm computes quantum max-mutual information in bipartite states, enhancing information processing capabilities.

Key Points

  • The goal is to compute the smooth max-mutual information of bipartite quantum states using semidefinite programming.
  • Developed an iterative algorithm based on semidefinite programming.
  • Established primal and dual formulations of the SDP.
  • Provided conditions for accuracy based on the rank of marginal states.
  • Algorithm accurately computes information measures when rank conditions are met.
  • Provides an upper bound for the computation when conditions are not satisfied.
  • Extends the application of SDP techniques in quantum information theory.

Cite This Study

Popp et al. (2026) studied this question.

synapsesocial.com/papers/699fe40c95ddcd3a253e8418https://doi.org/10.1007/s11128-026-05101-8
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Optimising the relative entropy under semidefinite constraints2026
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  4. 4Majorisation‐minimisation algorithm for optimal state discrimination in quantum communications2024
  5. 5Optimising the relative entropy under semi definite constraints -- A new tool for estimating key rates in QKD2024