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Minimum nonnegative TMSS squeezing, with singular cases corrected

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QAlgorithms.Papers.Independent.Intakethird100.P89.minimumTMSSSqueezing

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

prove2quantum

Let τ>0\tau>0τ>0, ∣1−τ∣≤y≤1+τ|1-\tau|\le y\le1+\tau∣1−τ∣≤y≤1+τ, and (τ,y)≠(1,0)(\tau,y)\ne(1,0)(τ,y)=(1,0). Define r0r_0r0​ independently as the infimum of nonnegative solutions sss to the actual TMSS noise equation

(1+τ)cosh⁡(2s)−2τsinh⁡(2s)=y.(1+\tau)\cosh(2s)-2\sqrt\tau\sinh(2s)=y.(1+τ)cosh(2s)−2τ​sinh(2s)=y.

This set has a least element, and

r0={12arcosh⁡ ⁣(y(1+τ)−2τ[y2−(1−τ)2](1−τ)2),τ≠1,12log⁡(2/y),τ=1.r_0=\begin{cases}\dfrac12\operatorname{arcosh}\!\left(\dfrac{y(1+\tau)-2\sqrt{\tau[y^2-(1-\tau)^2]}}{(1-\tau)^2}\right),&\tau\ne1,\\[4pt]\dfrac12\log(2/y),&\tau=1.\end{cases}r0​=⎩⎨⎧​21​arcosh((1−τ)2y(1+τ)−2τ[y2−(1−τ)2]​​),21​log(2/y),​τ=1,τ=1.​

A normalized pure TMSS of parameter r0r_0r0​, with covariance blocks A=B=cosh⁡(2r0)IA=B=\cosh(2r_0)IA=B=cosh(2r0​)I, C=−sinh⁡(2r0)σzC=-\sinh(2r_0)\sigma_zC=−sinh(2r0​)σz​, has logarithmic negativity 2r02r_02r0​ and finite per-mode photon number sinh⁡2r0\sinh^2 r_0sinh2r0​. At gain τ\sqrt\tauτ​ it simulates this channel on every input density. The identity has no finite nonnegative solution of the noise equation. On y=e−2r(1+τ)y=e^{-2r}(1+\tau)y=e−2r(1+τ), r≥0r\ge0r≥0, one has r0≥rr_0\ge rr0​≥r, with equality exactly when τ=1\tau=1τ=1 or r=0r=0r=0, and strict inequality for r>0r>0r>0, τ≠1\tau\ne1τ=1.

The unit-transmissivity extension and equality exceptions correct the printed singular formula and blanket strict comparison; no assertion of two distinct nonnegative roots is made.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P89
Formal statement
noncomputable section

open MeasureTheory Filter

open scoped BigOperators ENNReal Topology ComplexOrder

attribute [local instance] Classical.propDecidable Classical.decEq

namespace QAlgorithms.Papers.Independent.Intakethird100.P89

open QAlgorithms.Papers.Independent.Dependency50.P33
  (State IsDensity IsTraceClassChannel tensorApply traceSize)

open QAlgorithms.Papers.Independent.Dependency50.P39
  (coherentKet coherentCoefficient operatorOfMatrix husimiQ HasFockMatrix CoherentRealized)

open QAlgorithms.Papers.Independent.Frontier30.P14 (weylCharacteristic)

open QAlgorithms.Papers.Independent.Claimed50.P19
  (Cov FockIndex DensityKernel PhysicalCovariance CharacteristicSummable
   multimodeWeylTrace covarianceCharacteristicValue standardFormCovariance)

open QAlgorithms.Papers.Independent.Frontier30.P08 (QuadratureIndex)

open QAlgorithms.Papers.Independent.Gap48.P26 (PhaseInsensitive coherentKernel)

open QAlgorithms.Entanglement (partialTransposeFirst)

/-- PDF p.3, (10): minimum defined by the actual noise equation; finite
TMSS realization on every input. CORRECTIONS: tau=1 uses log(2/y)/2;
identity has no finite solution; the strict r0>r comparison has equality
at tau=1 or r=0. No assertion of two distinct nonnegative roots is made. -/
theorem minimumTMSSSqueezing (tau y : ℝ) (htau : 0 < tau)
    (hy : |1 - tau| ≤ y) (hyupper : y ≤ 1 + tau)
    (hidentity : tau ≠ 1 ∨ y ≠ 0) :
    IsLeast {s : ℝ | 0 ≤ s ∧ tmssNoise (Real.sqrt tau) s = y}
      (minimumSqueezing tau y) ∧
    minimumSqueezing tau y = squeezingClosedForm tau y ∧
    (∃ (rho : State (ℕ × ℕ)) (Phi : OscillatorChannel),
      rho.coefficients = tmssKernel (minimumSqueezing tau y) ∧
      CenteredResource rho (standardFormCovariance
        (Real.cosh (2 * minimumSqueezing tau y)) (Real.cosh (2 * minimumSqueezing tau y))
        (-Real.sinh (2 * minimumSqueezing tau y)) (Real.sinh (2 * minimumSqueezing tau y))) ∧
      twoModeMeanPhotonEnergy rho = ENNReal.ofReal (Real.sinh (minimumSqueezing tau y) ^ 2) ∧
      twoModeMeanPhotonEnergy rho < ⊤ ∧
      traceSize (partialTransposeFirst rho.coefficients) =
        ENNReal.ofReal (Real.exp (2 * minimumSqueezing tau y)) ∧
      IsBK rho (Real.sqrt tau) Phi ∧ PhaseInsensitive Phi tau y) ∧
    (¬ ∃ s : ℝ, 0 ≤ s ∧ tmssNoise 1 s = 0) ∧
    (∀ r : ℝ, 0 ≤ r → y = Real.exp (-2 * r) * (1 + tau) →
      r ≤ minimumSqueezing tau y ∧
      (minimumSqueezing tau y = r ↔ tau = 1 ∨ r = 0) ∧
      (0 < r → tau ≠ 1 → r < minimumSqueezing tau y)) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P89

end
Source
Optimal continuous variable quantum teleportation with limited resources, arXiv:1705.03017v5, PDF p. 3, Minimum TMSS squeezing for the non-entanglement-breaking range, Eq. (10)
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What the Lean code literally says, in plain math · gpt-6.1-sol

For real τ,y\tau,yτ,y satisfying τ>0\tau>0τ>0, ∣1−τ∣≤y≤1+τ|1-\tau|\le y\le1+\tau∣1−τ∣≤y≤1+τ, and τ≠1\tau\ne1τ=1 or y≠0y\ne0y=0, define

T(g,s)=(1+g2)cosh⁡(2s)−2gsinh⁡(2s),s∗=inf⁡{s∈R:s≥0, T(τ,s)=y}.T(g,s)=(1+g^2)\cosh(2s)-2g\sinh(2s),\qquad s_*=\inf\{s\in\mathbb R:s\ge0,\ T(\sqrt\tau,s)=y\}.T(g,s)=(1+g2)cosh(2s)−2gsinh(2s),s∗​=inf{s∈R:s≥0, T(τ​,s)=y}.

The number s∗s_*s∗​ belongs to this set and is at most every other member, and

s∗={12log⁡(2/y),τ=1,12log⁡ ⁣(x+x2−1),τ≠1,x=y(1+τ)−2τ(y2−(1−τ)2)(1−τ)2.s_*= \begin{cases} \frac12\log(2/y),&\tau=1,\\[2pt] \frac12\log\!\left(x+\sqrt{x^2-1}\right),&\tau\ne1, \end{cases} \qquad x=\frac{y(1+\tau)-2\sqrt{\tau\bigl(y^2-(1-\tau)^2\bigr)}}{(1-\tau)^2}.s∗​={21​log(2/y),21​log(x+x2−1​),​τ=1,τ=1,​x=(1−τ)2y(1+τ)−2τ(y2−(1−τ)2)​​.

To specify the resource assertion, a trace-class kernel on III means a kernel Xij=∑kuk(i)vk(j)‾X_{ij}=\sum_k u_k(i)\overline{v_k(j)}Xij​=∑k​uk​(i)vk​(j)​ with uk,vk∈ℓ2(I;C)u_k,v_k\in\ell^2(I;\mathbb C)uk​,vk​∈ℓ2(I;C) and ∑k∥uk∥∥vk∥<∞\sum_k\|u_k\|\|v_k\|<\infty∑k​∥uk​∥∥vk​∥<∞; a density kernel additionally has real nonnegative finite quadratic forms and a summable real diagonal of sum one. Let N(X)N(X)N(X) be the extended-nonnegative infimum of these norm-product sums, with empty infimum ∞\infty∞, and write [x]+=max⁡(x,0)[x]_+=\max(x,0)[x]+​=max(x,0) in [0,∞][0,\infty][0,∞]. There exist a two-mode density kernel RRR and a function Φ\PhiΦ on one-mode kernels with

Rab=ts∗(a)ts∗(b)‾,ts(a)=1a0=a1(−tanh⁡s)a0cosh⁡s,R_{ab}=t_{s_*}(a)\overline{t_{s_*}(b)},\qquad t_s(a)=\mathbf1_{a_0=a_1}\frac{(-\tanh s)^{a_0}}{\cosh s},Rab​=ts∗​​(a)ts∗​​(b)​,ts​(a)=1a0​=a1​​coshs(−tanhs)a0​​, V=(cosh⁡(2s∗)0−sinh⁡(2s∗)00cosh⁡(2s∗)0sinh⁡(2s∗)−sinh⁡(2s∗)0cosh⁡(2s∗)00sinh⁡(2s∗)0cosh⁡(2s∗)).V= \begin{pmatrix} \cosh(2s_*)&0&-\sinh(2s_*)&0\\ 0&\cosh(2s_*)&0&\sinh(2s_*)\\ -\sinh(2s_*)&0&\cosh(2s_*)&0\\ 0&\sinh(2s_*)&0&\cosh(2s_*) \end{pmatrix}.V=​cosh(2s∗​)0−sinh(2s∗​)0​0cosh(2s∗​)0sinh(2s∗​)​−sinh(2s∗​)0cosh(2s∗​)0​0sinh(2s∗​)0cosh(2s∗​)​​.

This VVV is symmetric positive definite and V+idiag⁡(J,J)V+i\operatorname{diag}(J,J)V+idiag(J,J) is positive semidefinite, J=(01−10)J=\left(\begin{smallmatrix}0&1\\-1&0\end{smallmatrix}\right)J=(0−1​10​). Define

Dmn(z)=e−∣z∣2/2m!n!∑j=0min⁡(m,n)zm−j(−z‾)n−jj!(m−j)!(n−j)!,χX(z)=∑m∑nXmnDnm(z),D_{mn}(z)=e^{-|z|^2/2}\sqrt{m!n!} \sum_{j=0}^{\min(m,n)} \frac{z^{m-j}(-\overline z)^{n-j}}{j!(m-j)!(n-j)!}, \quad \chi_X(z)=\sum_m\sum_nX_{mn}D_{nm}(z),Dmn​(z)=e−∣z∣2/2m!n!​j=0∑min(m,n)​j!(m−j)!(n−j)!zm−j(−z)n−j​,χX​(z)=m∑​n∑​Xmn​Dnm​(z), χR(2)(v)=∑a∑bRab∏j=01Dbjaj ⁣(−vj,1+ivj,02).\chi_R^{(2)}(v)=\sum_a\sum_bR_{ab} \prod_{j=0}^1D_{b_ja_j}\!\left(\frac{-v_{j,1}+iv_{j,0}}{\sqrt2}\right).χR(2)​(v)=a∑​b∑​Rab​j=0∏1​Dbj​aj​​(2​−vj,1​+ivj,0​​).

Every inner and outer series here is summable for every real vvv, and χR(2)(v)=e−vTVv/4\chi_R^{(2)}(v)=e^{-v^TVv/4}χR(2)​(v)=e−vTVv/4. Moreover

E(R):=∑p[p0+p12Re⁡Rpp]+=[sinh⁡2s∗]+<∞,N(RΓ)=[e2s∗]+,R(a,b),(c,d)Γ=R(c,b),(a,d).E(R):=\sum_p\left[\frac{p_0+p_1}{2}\operatorname{Re}R_{pp}\right]_+ =[\sinh^2s_*]_+<\infty,\qquad N(R^\Gamma)=[e^{2s_*}]_+, \quad R^\Gamma_{(a,b),(c,d)}=R_{(c,b),(a,d)}.E(R):=p∑​[2p0​+p1​​ReRpp​]+​=[sinh2s∗​]+​<∞,N(RΓ)=[e2s∗​]+​,R(a,b),(c,d)Γ​=R(c,b),(a,d)​.

The function Φ\PhiΦ preserves trace-class kernels, is additive and complex homogeneous on them, and preserves density kernels under every finite first-factor ampliation, including the vacuous dimension-zero test. For every one-mode density kernel SSS and every z∈Cz\in\mathbb Cz∈C,

χΦ(S)(z)=χS(τz) χR(2)(2τIm⁡z,2τRe⁡z,2Im⁡z,−2Re⁡z)=χS(τz)e−y∣z∣2/2.\chi_{\Phi(S)}(z)=\chi_S(\sqrt\tau z)\, \chi_R^{(2)}(\sqrt{2\tau}\operatorname{Im}z,\sqrt{2\tau}\operatorname{Re}z, \sqrt2\operatorname{Im}z,-\sqrt2\operatorname{Re}z) =\chi_S(\sqrt\tau z)e^{-y|z|^2/2}.χΦ(S)​(z)=χS​(τ​z)χR(2)​(2τ​Imz,2τ​Rez,2​Imz,−2​Rez)=χS​(τ​z)e−y∣z∣2/2.

The statement also asserts that no s≥0s\ge0s≥0 satisfies T(1,s)=0T(1,s)=0T(1,s)=0. Finally, for every r≥0r\ge0r≥0 with y=e−2r(1+τ)y=e^{-2r}(1+\tau)y=e−2r(1+τ), one has r≤s∗r\le s_*r≤s∗​, with s∗=rs_*=rs∗​=r if and only if τ=1\tau=1τ=1 or r=0r=0r=0; if r>0r>0r>0 and τ≠1\tau\ne1τ=1, then r<s∗r<s_*r<s∗​. The excluded case is exactly (τ,y)=(1,0)(\tau,y)=(1,0)(τ,y)=(1,0).

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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