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Proof of Theorem 1 — PX\mathcal P_XPX​ maps density matrices to density matrices

Proved
ProjectiveMeasurementEquilibration.pinching_isDensityMatrix

by Lucas · Oct 2, 2026 · Mathlib 0df444a (Lean v4.33.1)

density-matrixpinchingquantum-mechanics

Throughout, nnn is a finite index type, the Hilbert space is Cn\mathbb C^nCn, XXX is a Hermitian n×nn\times nn×n complex matrix with spectral decomposition X=∑xx PxX=\sum_{x} x\,P_xX=∑x​xPx​ (xxx ranging over the distinct eigenvalues, PxP_xPx​ the orthogonal projection onto the xxx-eigenspace, and Px=0P_x=0Px​=0 for xxx not an eigenvalue), and a density matrix is a positive semidefinite matrix of trace 111. If ρ\rhoρ is a density matrix then so is PX(ρ)=∑xPxρPx\mathcal P_X(\rho)=\sum_xP_x\rho P_xPX​(ρ)=∑x​Px​ρPx​ (the pinching is positive and trace preserving).

Preamble
import Mathlib
import Definitions.Def_pme_quantum_basics
import Definitions.Def_pme_measurement_conditions

open Matrix Filter Topology
Formal statement
namespace ProjectiveMeasurementEquilibration

theorem pinching_isDensityMatrix {n : Type} [Fintype n] [DecidableEq n] {X : Matrix n n ℂ} (hX : X.IsHermitian)
    (ρ : Matrix n n ℂ) (hρ : IsDensityMatrix ρ) :
    IsDensityMatrix (pinching hX ρ) := by sorry

end ProjectiveMeasurementEquilibration
Source
G. Carcassi (Assumptions of Physics collaboration), "Projective measurements are equilibration processes", AoP Technical Brief 017, https://assumptionsofphysics.org/resources/briefs, proof of Theorem 1 (EQ-HS ⇔ EQ-MEAS), p. 3
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What the Lean code literally says, in plain math · Aristotle (Harmonic) - same agent as the drafter; non-blind

Non-blind read-back. This read-back was written by the same agent (Aristotle, by Harmonic) that drafted the Lean statements, at the explicit request of the account owner. It is not blind and not independent testimony: the author knew the intended meaning while writing it. Reviewers should not treat it as an independent audit.

For every finite index type nnn, Hermitian X∈Mn(C)X\in M_n(\mathbb C)X∈Mn​(C) and density matrix ρ\rhoρ (positive semidefinite, tr⁡ρ=1\operatorname{tr}\rho=1trρ=1), the matrix ∑x∈ΛPxρPx\sum_{x\in\Lambda}P_x\rho P_x∑x∈Λ​Px​ρPx​ is positive semidefinite and has trace 111.

Human review
  • Endorsed by Shuze Chen · Oct 8, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Lucas · Oct 8, 2026

    Confirmed by the mission captain (proposal self-audit).

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