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Proposition 3: nonnegativity and vanishing on local boxes

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QAlgorithms.Papers.Independent.Intakethird100.P22.nonlocality_nonnegative_local

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.

Then

Nsq(P)≥0,P admits a local hidden-variable model ⟹ Nsq(P)=0.N_{\rm sq}(P)\ge0,\qquad P\text{ admits a local hidden-variable model}\ \Longrightarrow\ N_{\rm sq}(P)=0.Nsq​(P)≥0,P admits a local hidden-variable model ⟹ Nsq​(P)=0.

Vanishing on local devices is one-way and asserts no faithfulness converse.

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.45, Proposition 3, (219): nonnegative and zero on local boxes;
no converse, optimizer, or favorable extension is assumed. Source convention: Eve keeps the dice
result (p.22, XI.B.3); CE members may depend on (z,e) -/
theorem nonlocality_nonnegative_local {p : Box X Y A B} (c : CompleteExtension p) :
    0 ≤ squashedNonlocality c ∧ (HasLocalModel p → squashedNonlocality c = 0) := 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. 45, Proposition 3 / equation (219)
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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 0≤N(c)0\le N(c)0≤N(c), and, if ppp has a local model, then N(c)=0N(c)=0N(c)=0. A complete extension consists of positive finite index sets z,ez,ez,e, common valid boxes vev_eve​, and probability weights wzew_{ze}wze​ reconstructing p=∑ewzevep=\sum_e w_{ze}v_ep=∑e​wze​ve​ for every zzz; valid means nonnegative, normalized at every input, and no-signaling in both directions. Each positive-weight support consists of vertices and has no proper subset supporting any finite valid decomposition of ppp, and each such minimal vertex decomposition is represented by exactly one zzz, with equality of total mass at every box. For probability weights dzd_zdz​, define 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); then

N(c)=max⁡x,yinf⁡dinf⁡m>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),N(c)=\max_{x,y}\inf_d\inf_{\substack{m>0\\K(e,j)\ge0,\ \sum_jK(e,j)=1}} \bigl[H(s_{AJ})+H(s_{BJ})-H(s_J)-H(s)\bigr], \qquad s(a,b,j)=\sum_er_d^{xy}(a,b,e)K(e,j),N(c)=x,ymax​dinf​m>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),

where H(r)=−∑irilog⁡2riH(r)=-\sum_ir_i\log_2r_iH(r)=−∑i​ri​log2​ri​. Having a local model means that ppp is nonnegative and normalized and that there exist a type LLL, a probability mass function μ\muμ on LLL, and probability response vectors αl(⋅∣x)\alpha_l(\cdot\mid x)αl​(⋅∣x), βl(⋅∣y)\beta_l(\cdot\mid y)βl​(⋅∣y) such that p(x,y,a,b)=∑l′μ(l)αl(a∣x)βl(b∣y)p(x,y,a,b)=\sum'_l\mu(l)\alpha_l(a\mid x)\beta_l(b\mid y)p(x,y,a,b)=∑l′​μ(l)αl​(a∣x)βl​(b∣y); LLL is not required to be finite.

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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