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feat(QuantumInfo): Fisher-Rao metric and Cramer-Rao bound on finite simplex - #1652

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naype888-cloud:nava-fisher-rao-cramer-rao

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Summary

  • Define the Fisher–Rao inner product on the open probability simplex
  • Prove Cauchy–Schwarz for the Fisher–Rao inner product (sorry-free)
  • Derive the classical Cramér–Rao lower bound on estimator variance (sorry-free)
  • Single new file QuantumInfo/ForMathlib/FisherRao.lean plus its import line

t-quantum-info

Definitions added

Name Meaning
FisherRao.OpenSimplex Point on the open probability simplex Δ°(α)
FisherRao.OpenSimplex.fisherRaoInner gₚ(u, v) = Σᵢ uᵢvᵢ/pᵢ
FisherRao.fisherInfo Classical Fisher information I(θ) = Σᵢ (∂pᵢ/∂θ)²/pᵢ
FisherRao.expect Weighted expectation E_p[f]
FisherRao.variance Weighted variance Var_p(f)

Theorems

Name Statement
fisherRao_cauchy_schwarz gₚ(u,v)² ≤ gₚ(u,u) · gₚ(v,v)
fisherRaoSq_eq_zero_iff Positive definiteness of the Fisher–Rao form
fisherInfo_eq_fisherRaoSq I(θ) = ‖dp/dθ‖²_FR
cramerRao Var(T) ≥ 1/I(θ) for unbiased estimators

Context

Neither Mathlib nor PhysLean currently have Fisher information, the Fisher–Rao metric, or the Cramér–Rao bound formalized. This PR fills that gap using only Mathlib primitives (Finset.sum, div, field_simp).

The Cramér–Rao proof reduces to Cauchy–Schwarz on the Fisher–Rao inner product — the same obstruction kernel underlying the Robertson–Schrödinger uncertainty relation already in PhysLean.

Test plan

  • CI passes (Build, Lint, imports check)
  • No sorry, no new axioms beyond standard

AI-assisted-by: Anthropic Claude

…implex

Define the Fisher–Rao inner product on the open probability simplex,
prove Cauchy–Schwarz for it, and derive the classical Cramér–Rao
lower bound on estimator variance. All proofs are sorry-free and use
only Mathlib primitives.

New file `QuantumInfo/ForMathlib/FisherRao.lean` plus its import line.

## Definitions

* `FisherRao.OpenSimplex` — point on Δ°(α) with strictly positive weights
* `FisherRao.OpenSimplex.fisherRaoInner` — gₚ(u,v) = Σᵢ uᵢvᵢ/pᵢ
* `FisherRao.fisherInfo` — I(θ) = Σᵢ (∂pᵢ/∂θ)²/pᵢ
* `FisherRao.expect`, `FisherRao.variance` — weighted expectation/variance

## Theorems

* `fisherRao_cauchy_schwarz` — gₚ(u,v)² ≤ gₚ(u,u)·gₚ(v,v)
* `fisherRaoSq_eq_zero_iff` — positive definiteness
* `fisherInfo_eq_fisherRaoSq` — I(θ) = ‖dp/dθ‖²_FR
* `cramerRao` — Var(T) ≥ 1/I(θ) for unbiased estimators
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Thank you for this pull-request (PR). If this is your first PR, welcome to the community!

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@naype888-cloud

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This PR formalizes the Fisher–Rao metric and the Cramér–Rao bound for finite sample spaces — neither exists in Mathlib or PhysLean today.

Why here: The Cramér–Rao proof reduces to Cauchy–Schwarz on a weighted inner product, which is the same obstruction kernel behind Robertson–Schrödinger (already in PhysLean via Cipollina and Diem). Having Fisher–Rao in ForMathlib/ makes it available as infrastructure for quantum information geometry — in particular, connecting the algebraic uncertainty framework to the Fubini-Study/Fisher-Rao bridge that @Zayn described in the Integrating QuantumInfo thread.

What it gives you:

  • fisherRaoInner — the metric on the open simplex, with positive definiteness proved
  • fisherRao_cauchy_schwarz — sorry-free
  • cramerRao — sorry-free, derived from the above
  • fisherInfo_eq_fisherRaoSq — Fisher information = FR squared norm of the score vector
    No new axioms, no sorry, Mathlib-only imports. Happy to adjust naming or placement if reviewers prefer a different location.

atte:Eduardo Nava Hernandez (Lalo)

@naype888-cloud

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im herre to write the personal coment, by mysself, i do this to help @zyan to complete his own comit, about bloch sphere i think. so i need it to, becouse the uncerntanly dimensional elemental principle can be fited here on Hilbert 4+

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Closing in favor of #1657, which ports this content to PhyslibAlpha.AlgebraicFramework.InformationGeometry.FisherRao (the intended home for the estimation-geometry layer, per maintainer feedback in the 'Fisher information and Fubini-Study' thread). This QuantumInfo version is superseded; please review #1657 instead.

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