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Proposition 4: convexity

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QAlgorithms.Papers.Independent.Intakethird100.P22.nonlocality_convex

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.

For two devices P,QP,QP,Q in the same fixed polytope and every λ∈[0,1]\lambda\in[0,1]λ∈[0,1], with the mixture computed pointwise,

Nsq(λP+(1−λ)Q)≤λNsq(P)+(1−λ)Nsq(Q).N_{\rm sq}(\lambda P+(1-\lambda)Q)\le\lambda N_{\rm sq}(P)+(1-\lambda)N_{\rm sq}(Q).Nsq​(λP+(1−λ)Q)≤λNsq​(P)+(1−λ)Nsq​(Q).

The claim holds for arbitrary complete extensions of each indicated device and includes both endpoints.

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.46, Proposition 4, (222): convexity, including both endpoints.
The CE arguments are arbitrary complete extensions of the specified devices. Source convention: Eve keeps the dice
result (p.22, XI.B.3); CE members may depend on (z,e) -/
theorem nonlocality_convex (p q : Box X Y A B) (t : ℝ) (ht : 0 ≤ t) (htOne : t ≤ 1)
    (cp : CompleteExtension p) (cq : CompleteExtension q)
    (cm : CompleteExtension (correlationMixture p q t)) :
    squashedNonlocality cm ≤ t * squashedNonlocality cp + (1-t) * squashedNonlocality cq := 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. 46, Proposition 4 / equation (222)
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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 real arrays p,qp,qp,q, any real ttt with 0≤t≤10\le t\le10≤t≤1, and any complete extensions cp,cq,cmc_p,c_q,c_mcp​,cq​,cm​ of p,q,mp,q,mp,q,m, respectively, where m(x,y,a,b)=t p(x,y,a,b)+(1−t)q(x,y,a,b)m(x,y,a,b)=t\,p(x,y,a,b)+(1-t)q(x,y,a,b)m(x,y,a,b)=tp(x,y,a,b)+(1−t)q(x,y,a,b), the assertion is

N(cm)≤t N(cp)+(1−t)N(cq).N(c_m)\le t\,N(c_p)+(1-t)N(c_q).N(cm​)≤tN(cp​)+(1−t)N(cq​).

Here a complete extension ccc supplies positive finite indices z,ez,ez,e, probability weights wzew_{ze}wze​, and common nonnegative normalized no-signaling boxes vev_eve​ reconstructing its box for every zzz. Every positive-weight support consists of vertices and has no proper subset supporting another finite valid decomposition, and every such minimal vertex decomposition has a unique zzz with the same total weight at every box. For each such extension,

N(c)=max⁡x,yinf⁡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(x,y,a,b)K(e,j),N(c)=\max_{x,y}\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(x,y,a,b)K(e,j),N(c)=x,ymax​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​(x,y,a,b)K(e,j),

with j<ℓj<\ellj<ℓ, marginals formed by summation, and H(r)=−∑irilog⁡2riH(r)=-\sum_ir_i\log_2r_iH(r)=−∑i​ri​log2​ri​. The endpoints t=0t=0t=0 and t=1t=1t=1 are included, and the three complete extensions are quantified independently.

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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