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Observation 2: hierarchy with MI and CMI

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QAlgorithms.Papers.Independent.Intakethird100.P22.information_hierarchy

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

prove2quantum

All input and output alphabets are finite and nonempty. Let P(a,b∣x,y)P(a,b\mid x,y)P(a,b∣x,y) be a normalized nonnegative bipartite non-signaling device, and let E(P)\mathcal E(P)E(P) be its complete extension, with possibly input-dependent pure-member labels. All Shannon information quantities are in bits, with 0log⁡20=00\log_2 0=00log2​0=0. Write Nsq(P)N_{\rm sq}(P)Nsq​(P) for the maximum over honest settings (x,y)(x,y)(x,y) of the infimum, over Eve's general measurements, of intrinsic information; intrinsic information itself minimizes classical conditional mutual information over arbitrary stochastic channels on Eve.

Write I^(A:B)\widehat I(A:B)I(A:B) and I^(A:B∣E)\widehat I(A:B\mid E)I(A:B∣E) for squashing classical mutual information and conditional mutual information, respectively, on the same complete extension and in the same max-then-inf order. Then both comparisons hold:

Nsq(P)≤I^(A:B),Nsq(P)≤I^(A:B∣E).N_{\rm sq}(P)\le\widehat I(A:B),\qquad N_{\rm sq}(P)\le\widehat I(A:B\mid E).Nsq​(P)≤I(A:B),Nsq​(P)≤I(A:B∣E).

Each is an upper bound on the source-defined squashed intrinsic information.

Eve retains the dice result zzz and extension output eee. Squashed CMI conditions on (z,e)(z,e)(z,e); intrinsic-information channels may depend on both. This is the operational convention of XI.B.3 used by the proof, rather than discarding the dice result in display (7).

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P22
Formal statement
noncomputable section

open scoped BigOperators

namespace QAlgorithms.Papers.Independent.Intakethird100.P22

attribute [local instance] Classical.propDecidable Classical.decEq

open QAlgorithms.Papers.Independent.Advance50.P47
  (Box IsCorrelation IsNoSignaling HasLocalModel correlationMixture prBox)

open QAlgorithms.Papers.Independent.Information100.P56 (IsLaw)

open QAlgorithms.Papers.Independent.Information100.P27 (shannon)

open QAlgorithms.Papers.Independent.Gap48.P25 (classicalMI)

section Devices

variable {X Y A B : Type} [Fintype X] [Fintype Y] [Fintype A] [Fintype B]
  [Nonempty X] [Nonempty Y] [Nonempty A] [Nonempty B]

/-- PDF p.54, Observation 2, (311),(312): both hierarchy comparisons. Source convention: Eve keeps the dice
result (p.22, XI.B.3); CE members may depend on (z,e) -/
theorem information_hierarchy {p : Box X Y A B} (c : CompleteExtension p) :
    squashedNonlocality c ≤ squashedMI c ∧ squashedNonlocality c ≤ squashedCMI c := by sorry

end Devices

end QAlgorithms.Papers.Independent.Intakethird100.P22

end
Source
Limitations on device independent key secure against non signaling adversary via the squashed non-locality, arXiv:1903.12154v7, PDF p. 54, Observation 2 / equations (311), (312)
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What the Lean code literally says, in plain math · gpt-6.1-sol

For finite nonempty alphabets X,Y,A,BX,Y,A,BX,Y,A,B, any box ppp, and any complete extension ccc of ppp, the assertion is

N(c)≤M(c)andN(c)≤C(c).N(c)\le M(c)\qquad\text{and}\qquad N(c)\le C(c).N(c)≤M(c)andN(c)≤C(c).

Here ccc consists of positive finite indices z,ez,ez,e, common nonnegative normalized no-signaling boxes vev_eve​, and probability weights wzew_{ze}wze​ reconstructing ppp for every zzz. Each positive-weight support consists of vertices and has no proper subset supporting another finite valid decomposition; every such minimal vertex decomposition has exactly one index zzz with the same total mass at every box. For probability weights dzd_zdz​, put rdxy(a,b,e)=∑zdzwzeve(x,y,a,b)r_d^{xy}(a,b,e)=\sum_zd_zw_{ze}v_e(x,y,a,b)rdxy​(a,b,e)=∑z​dz​wze​ve​(x,y,a,b). With H(r)=−∑irilog⁡2riH(r)=-\sum_ir_i\log_2r_iH(r)=−∑i​ri​log2​ri​, define

M(c)=max⁡x,yinf⁡d[H((rdxy)A)+H((rdxy)B)−H((rdxy)AB)],C(c)=max⁡x,yinf⁡d[H((rdxy)AE)+H((rdxy)BE)−H((rdxy)E)−H(rdxy)],N(c)=max⁡x,yinf⁡dinf⁡ℓ>0K(e,j)≥0, ∑jK(e,j)=1[H(sAJ)+H(sBJ)−H(sJ)−H(s)],s(a,b,j)=∑erdxy(a,b,e)K(e,j).\begin{aligned} M(c)&=\max_{x,y}\inf_d \bigl[H((r_d^{xy})_A)+H((r_d^{xy})_B)-H((r_d^{xy})_{AB})\bigr],\\ C(c)&=\max_{x,y}\inf_d \bigl[H((r_d^{xy})_{AE})+H((r_d^{xy})_{BE})-H((r_d^{xy})_E)-H(r_d^{xy})\bigr],\\ N(c)&=\max_{x,y}\inf_d\inf_{\substack{\ell>0\\K(e,j)\ge0,\ \sum_jK(e,j)=1}} \bigl[H(s_{AJ})+H(s_{BJ})-H(s_J)-H(s)\bigr], \quad s(a,b,j)=\sum_er_d^{xy}(a,b,e)K(e,j). \end{aligned}M(c)C(c)N(c)​=x,ymax​dinf​[H((rdxy​)A​)+H((rdxy​)B​)−H((rdxy​)AB​)],=x,ymax​dinf​[H((rdxy​)AE​)+H((rdxy​)BE​)−H((rdxy​)E​)−H(rdxy​)],=x,ymax​dinf​ℓ>0K(e,j)≥0, ∑j​K(e,j)=1​inf​[H(sAJ​)+H(sBJ​)−H(sJ​)−H(s)],s(a,b,j)=e∑​rdxy​(a,b,e)K(e,j).​

All subscripts denote marginals obtained by summation, and jjj ranges over a finite set of size ℓ\ellℓ.

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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