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Proposition 8: support-sensitive subextensivity

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QAlgorithms.Papers.Independent.Intakethird100.P22.nonlocality_subextensive

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.

Let dA=max⁡x∣{a:PA(a∣x)>0}∣d_A=\max_x|\{a:P_A(a\mid x)>0\}|dA​=maxx​∣{a:PA​(a∣x)>0}∣ and dB=max⁡y∣{b:PB(b∣y)>0}∣d_B=\max_y|\{b:P_B(b\mid y)>0\}|dB​=maxy​∣{b:PB​(b∣y)>0}∣ be the maximum support sizes of the computed no-signaling marginals. Then

Nsq(P)≤log⁡2min⁡{dA,dB}.N_{\rm sq}(P)\le\log_2\min\{d_A,d_B\}.Nsq​(P)≤log2​min{dA​,dB​}.

Both support sizes are at least one. Reference inputs used to compute the marginals are arbitrary and do not affect their values.

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.52, Proposition 8, (293): support dimensions, not ambient cardinalities.
Normalization and nonempty inputs imply both log arguments are at least one. Source convention: Eve keeps the dice
result (p.22, XI.B.3); CE members may depend on (z,e) -/
theorem nonlocality_subextensive {p : Box X Y A B} (c : CompleteExtension p) (x : X) (y : Y) :
    squashedNonlocality c ≤ Real.logb 2 (min (aliceSupport p y) (bobSupport p x) : ℕ) := 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. 52, Proposition 8 / equation (293)
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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, any complete extension ccc of ppp, and every specified pair x∈X,y∈Yx\in X,y\in Yx∈X,y∈Y, the assertion is

N(c)≤log⁡2 ⁣(min⁡{max⁡x′∈X∣{a:∑bp(x′,y,a,b)>0}∣,max⁡y′∈Y∣{b:∑ap(x,y′,a,b)>0}∣}).N(c)\le \log_2\!\left( \min\left\{ \max_{x'\in X}\left|\left\{a:\sum_bp(x',y,a,b)>0\right\}\right|, \max_{y'\in Y}\left|\left\{b:\sum_ap(x,y',a,b)>0\right\}\right| \right\}\right).N(c)≤log2​(min{x′∈Xmax​​{a:b∑​p(x′,y,a,b)>0}​,y′∈Ymax​​{b:a∑​p(x,y′,a,b)>0}​}).

A complete extension 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. Its positive-weight supports consist of vertices, have no proper subset supporting another finite valid decomposition, and uniquely enumerate all such minimal vertex decompositions by their mass at every box. The quantity on the left is

N(c)=max⁡u,vinf⁡dz≥0∑zdz=1inf⁡ℓ>0K(e,j)≥0, ∑jK(e,j)=1[H(sAJ)+H(sBJ)−H(sJ)−H(s)],s(a,b,j)=∑z,edzwzeve(u,v,a,b)K(e,j),N(c)=\max_{u,v}\inf_{\substack{d_z\ge0\\\sum_zd_z=1}} \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_{z,e}d_zw_{ze}v_e(u,v,a,b)K(e,j),N(c)=u,vmax​dz​≥0∑z​dz​=1​inf​ℓ>0K(e,j)≥0, ∑j​K(e,j)=1​inf​[H(sAJ​)+H(sBJ​)−H(sJ​)−H(s)],s(a,b,j)=z,e∑​dz​wze​ve​(u,v,a,b)K(e,j),

where j<ℓj<\ellj<ℓ, subscripts denote marginals, and H(r)=−∑irilog⁡2riH(r)=-\sum_ir_i\log_2r_iH(r)=−∑i​ri​log2​ri​. Both support counts use strict positivity; the logarithm is applied to their minimum after conversion to a real number.

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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