The mixture-sequential quantum probability ratio test achieves optimal Type-I and worst-case Type-II error exponents in composite sequential quantum hypothesis testing, characterized by minimal measured relative entropies, with a matching converse.
On error exponents in quantum hypothesis testing
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Quantum neural estimators achieve minimax-optimal copy complexity O(|Θ(U)| d / ε²) with sub-Gaussian concentration for measured Rényi relative entropies on density pairs with bounded Thompson metric.
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Optimal Error Exponents for Composite Sequential Quantum Hypothesis Testing
The mixture-sequential quantum probability ratio test achieves optimal Type-I and worst-case Type-II error exponents in composite sequential quantum hypothesis testing, characterized by minimal measured relative entropies, with a matching converse.
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Performance Guarantees for Quantum Neural Estimation of Entropies
Quantum neural estimators achieve minimax-optimal copy complexity O(|Θ(U)| d / ε²) with sub-Gaussian concentration for measured Rényi relative entropies on density pairs with bounded Thompson metric.