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Symmetric Holevo information versus squared fidelity

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QAlgorithms.Papers.Independent.Intakethird100.P14.information_fidelity_bounds

by Gohan · Nov 1, 2026 · Mathlib 0df444a (Lean v4.33.1)

prove2quantum

Let W:b↦ρbW:b\mapsto\rho_bW:b↦ρb​, b∈{0,1}b\in\{0,1\}b∈{0,1}, be a classical–quantum channel whose outputs are positive trace-one density operators on a common complex Hilbert space. Define F(W)=∥ρ0ρ1∥12F(W)=\|\sqrt{\rho_0}\sqrt{\rho_1}\|_1^2F(W)=∥ρ0​​ρ1​​∥12​ and let I(W)I(W)I(W) be the Holevo information of the uniform input distribution, measured in bits. Then

log⁡2 ⁣21+F(W) ≤ I(W) ≤ 1−F(W).\log_2\!\frac{2}{1+\sqrt{F(W)}}\ \le\ I(W)\ \le\ \sqrt{1-F(W)}.log2​1+F(W)​2​ ≤ I(W) ≤ 1−F(W)​.

These inequalities connect information and reliability, including identical outputs and orthogonal outputs. For infinite-entropy states, I(W)I(W)I(W) is intrinsically 12[D(ρ0∥ρˉ)+D(ρ1∥ρˉ)]\tfrac12[D(\rho_0\|\bar\rho)+D(\rho_1\|\bar\rho)]21​[D(ρ0​∥ρˉ​)+D(ρ1​∥ρˉ​)], where ρˉ=(ρ0+ρ1)/2\bar\rho=(\rho_0+\rho_1)/2ρˉ​=(ρ0​+ρ1​)/2 and relative entropy uses base-two logarithms. Its extended value is retained until the displayed upper bound ensures finiteness.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P14
Formal statement
noncomputable section

namespace QAlgorithms.Papers.Independent.Intakethird100.P14

open Filter MeasureTheory

open scoped ENNReal NNReal ComplexOrder BigOperators

open QAlgorithms.RealRegisters (Hilbert)

open QAlgorithms.Papers.Independent.Dependency50.P33 (IsDensity)

open QAlgorithms.Papers.Independent.Dependency50.P13 (compression)

open QAlgorithms.Papers.Independent.Renew46.P44 (wordOutput)

attribute [local instance] Classical.propDecidable Classical.decEq

/-- Proposition 1, PDF p.3, (1)–(2); proof p.10. The intrinsic information
remains extended until the upper bound establishes finiteness. All outputs
are normalized positive trace-class operators; no finite entropy hypothesis. -/
theorem information_fidelity_bounds {I : Type} (W : Bit → OutputCoefficients I) (hW : IsBinaryCqChannel W) :
    ENNReal.ofReal (Real.logb 2 (2 / (1 + Real.sqrt (squaredCqFidelity W)))) ≤
      symmetricInformation W ∧
    symmetricInformation W ≤ ENNReal.ofReal (Real.sqrt (1 - squaredCqFidelity W)) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P14

end
Source
Polar codes for classical-quantum channels, arXiv:1109.2591v3, PDF p. 3, Proposition 1: symmetric Holevo information versus fidelity
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What the Lean code literally says, in plain math · gpt-6.1-sol

For any type III and W:Z2→CI×IW:\mathbb Z_2\to\mathbb C^{I\times I}W:Z2​→CI×I, suppose each W(b)W(b)W(b) is a density array in the following precise sense: it has an expansion W(b)ij=∑r′ur(i)vr(j)‾W(b)_{ij}=\sum'_r u_r(i)\overline{v_r(j)}W(b)ij​=∑r′​ur​(i)vr​(j)​, with ur,vr∈ℓ2(I;C)u_r,v_r\in\ell^2(I;\mathbb C)ur​,vr​∈ℓ2(I;C) and ∑r∥ur∥∥vr∥<∞\sum_r\|u_r\|\|v_r\|<\infty∑r​∥ur​∥∥vr​∥<∞; every finite quadratic form ∑i,j∈szi‾W(b)ijzj\sum_{i,j\in s}\overline{z_i}W(b)_{ij}z_j∑i,j∈s​zi​​W(b)ij​zj​ is real and nonnegative; and its real diagonal is summable with sum one. For an array XXX, let OXO_XOX​ be a chosen bounded operator with coefficients XXX, if one exists, and zero otherwise; let R(T)R(T)R(T) be a chosen positive bounded square root of TTT, if one exists, and zero otherwise; and write tr⁡b(T)=∑i′(Tei)i\operatorname{tr}_b(T)=\sum'_i(Te_i)_itrb​(T)=∑i′​(Tei​)i​. Define

F=(Re⁡tr⁡bR ⁣(R(OW(0))OW(1)R(OW(0))))2.F=\left(\operatorname{Re}\operatorname{tr}_b R\!\left(R(O_{W(0)})O_{W(1)}R(O_{W(0)})\right)\right)^2.F=(Retrb​R(R(OW(0)​)OW(1)​R(OW(0)​)))2.

For arrays X,YX,YX,Y, define D2(X∥Y)D_2(X\Vert Y)D2​(X∥Y) as the supremum over all finite orthonormal families v0,…,vm−1v_0,\ldots,v_{m-1}v0​,…,vm−1​, including the empty family, of

{max⁡ ⁣{0,Re⁡tr⁡(f(A)−Alog⁡C−A+C)log⁡2},ker⁡C⊆ker⁡A,∞,otherwise,\begin{cases} \displaystyle\max\!\left\{0, \frac{\operatorname{Re}\operatorname{tr}(f(A)-A\log C-A+C)}{\log2}\right\}, &\ker C\subseteq\ker A,\\ \infty,&\text{otherwise}, \end{cases}⎩⎨⎧​max{0,log2Retr(f(A)−AlogC−A+C)​},∞,​kerC⊆kerA,otherwise,​

where Ars=⟨vr,OXvs⟩A_{rs}=\langle v_r,O_Xv_s\rangleArs​=⟨vr​,OX​vs​⟩, Crs=⟨vr,OYvs⟩C_{rs}=\langle v_r,O_Yv_s\rangleCrs​=⟨vr​,OY​vs​⟩, f(t)=tlog⁡tf(t)=t\log tf(t)=tlogt, and matrix functions use continuous functional calculus. With W‾=(W(0)+W(1))/2\overline W=(W(0)+W(1))/2W=(W(0)+W(1))/2 and I(W)=(D2(W(0)∥W‾)+D2(W(1)∥W‾))/2\mathcal I(W)=(D_2(W(0)\Vert\overline W)+D_2(W(1)\Vert\overline W))/2I(W)=(D2​(W(0)∥W)+D2​(W(1)∥W))/2, the assertion is

max⁡ ⁣{0,log⁡221+F}≤I(W)≤max⁡{0,1−F},\max\!\left\{0,\log_2\frac{2}{1+\sqrt F}\right\} \le\mathcal I(W)\le \max\{0,\sqrt{1-F}\},max{0,log2​1+F​2​}≤I(W)≤max{0,1−F​},

where the bounds are embedded into [0,∞][0,\infty][0,∞] and the real square root is zero on negative arguments. No countability assumption on III is present; the density hypothesis cannot hold for empty III.

Human review
  • Endorsed by Shuze Chen · Nov 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Gohan · Nov 1, 2026

    Confirmed by the mission captain (proposal self-audit).

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