Bayesian monotone metrics extend Petz metrics to prior-averaged states, yielding computable lower bounds on multiparameter Bayes risk that dominate van Trees bounds and can be optimized via a one-parameter subfamily.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A differentiable SDP method generates positive non-decomposable maps, identifies parametrized families, and explores open problems like the PPT square conjecture.
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Bayesian Monotone Metrics for Multiparameter Quantum Estimation
Bayesian monotone metrics extend Petz metrics to prior-averaged states, yielding computable lower bounds on multiparameter Bayes risk that dominate van Trees bounds and can be optimized via a one-parameter subfamily.
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Generating Non-Decomposable Maps with Differentiable Semidefinite Programming
A differentiable SDP method generates positive non-decomposable maps, identifies parametrized families, and explores open problems like the PPT square conjecture.