The Optimal Rate Function in Covariant Quantum State Tomography

• URL: http://arxiv.org/abs/2606.16948v1
• Date: Mon, 15 Jun 2026 16:47:46 GMT
• Title: The Optimal Rate Function in Covariant Quantum State Tomography
• Abstract summary: We characterize quantum tomography protocols by their emphrate function<n>This function governs the exponential rate at which a protocol assigns probability to a particular estimate $$ of the true state $$.<n>For any $$ and $$ we construct a family of protocols whose rate functions converge to the quantum relative entropy $D(|)$.
• Score: 40.87574349486165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The problem of quantum tomography is to estimate an unknown quantum state $ρ$ from a measurement of $n$ copies of $ρ$. One can ask which tomography protocol, i.e.\ which choice of multi-copy measurement, gives the best possible estimate of $ρ$. To do so, we characterize tomography protocols by their \emph{rate function}, which governs the exponential rate at which a protocol assigns probability to a particular estimate $σ$ of the true state $ρ$. This rate function is a quantum mechanical generalization of the classical relative entropy between the true state and its estimate, and depends on the choice of protocol. It is bounded by the quantum relative entropy, and we show that this bound is sharp: for any $ρ$ and $σ$ we construct a family of protocols whose rate functions converge to the quantum relative entropy $D(σ\|ρ)$. We consider the family of covariant tomography protocols; these are the basis independent state estimation schemes that assume no prior information about $ρ$ and $σ$. Keyl described a specific tomography protocol based on Schur sampling, and conjectured that among all covariant tomography protocols it has the largest possible rate function for all $σ$ and $ρ$. We prove this conjecture. The resulting rate function is an annealed version of quantum relative entropy, due to the cost of learning the eigenbasis in covariant quantum state tomography.

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