pith:J3PQBABH
Performance Guarantees for Quantum Neural Estimation of Entropies
Quantum neural estimators achieve O(d/ε²) copy complexity for measured Rényi relative entropies when density pairs have bounded Thompson metric.
arxiv:2511.19289 v2 · 2025-11-24 · quant-ph · cs.IT · cs.LG · math.IT
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Claims
For an appropriate sub-class of density operator pairs on a space of dimension d with bounded Thompson metric, our theory establishes a copy complexity of O(|Θ(U)|d/ε²) for QNE with a quantum circuit parameter set Θ(U), which has minimax optimal dependence on the accuracy ε.
The central bounds rely on the density operator pairs having bounded Thompson metric (or being permutation invariant); without this restriction the stated copy complexity no longer holds and the analysis does not apply.
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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| First computed | 2026-05-17T23:39:04.609184Z |
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4edf0080277122db77817160a706052dbbfb4dca56e7088fc0b47d7263bcf2ab
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Canonical record JSON
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