Sandwiched Renyi Entropy DPI gt one
sandwichedRenyiEntropy_DPI_gt_one
Plain-language statement
The Data Processing Inequality for the Sandwiched Rényi relative entropy (α > 1). Every CPTP map Φ satisfies D̃_α(Φρ‖Φσ) ≤ D̃_α(ρ‖σ). The proof uses the Stinespring representation (see CPTPMap.exists_purify): every CPTP map can be written as ancilla preparation + unitary conjugation + partial trace. Since the sandwiched Rényi divergence is invariant...
Exact Lean statement
theorem sandwichedRenyiEntropy_DPI_gt_one (hα : 1 < α) (ρ σ : MState d₁) (Φ : CPTPMap d₁ d₂) :
D̃_ α(Φ ρ‖Φ σ) ≤ D̃_ α(ρ‖σ)Formal artifact
Lean source
theorem sandwichedRenyiEntropy_DPI_gt_one (hα : 1 < α) (ρ σ : MState d₁) (Φ : CPTPMap d₁ d₂) : D̃_ α(Φ ρ‖Φ σ) ≤ D̃_ α(ρ‖σ) := by have _ : Nonempty d₁ := ρ.nonempty have _ : Nonempty d₂ := (Φ ρ).nonempty haveI : Inhabited d₂ := Classical.inhabited_of_nonempty ‹_› let ψ₀ : Ket (d₂ × d₂) := Ket.basis default let τ := MState.pure ψ₀ obtain ⟨U, hU⟩ := Φ.purify_IsUnitary -- USe the `zero_prep` / `prep` / `append` from `CPTPMap.purify_trace` let zero_prep : CPTPMap Unit (d₂ × d₂) := CPTPMap.replacement τ let prep := ((CPTPMap.id : CPTPMap d₁ d₁) ⊗ᶜᵖ zero_prep) let append : CPTPMap d₁ (d₁ × Unit) := CPTPMap.ofEquiv (Equiv.prodPUnit d₁).symm calc D̃_ α(Φ ρ‖Φ σ) _ = D̃_ α((Φ.purify ((prep ∘ₘ append) ρ)).traceLeft.traceLeft‖ (Φ.purify ((prep ∘ₘ append) σ)).traceLeft.traceLeft) := by have h_trace (ξ) : Φ ξ = (Φ.purify ((prep ∘ₘ append) ξ)).traceLeft.traceLeft := by simpa using congr($Φ.purify_trace ξ) rw [h_trace ρ, h_trace σ] _ = D̃_ α(((ρ ⊗ᴹ τ).U_conj U).traceLeft.traceLeft‖ ((σ ⊗ᴹ τ).U_conj U).traceLeft.traceLeft) := by have h_app (ξ) : Φ.purify ξ = ξ.U_conj U := congr($hU ξ) rw [prep_append_eq_tensor_pure ρ, prep_append_eq_tensor_pure σ, h_app, h_app] _ ≤ D̃_ α(((ρ ⊗ᴹ τ).U_conj U).traceLeft‖((σ ⊗ᴹ τ).U_conj U).traceLeft) := sandwichedRenyiEntropy_mono_traceLeft hα .. _ ≤ D̃_ α((ρ ⊗ᴹ τ).U_conj U‖(σ ⊗ᴹ τ).U_conj U) := sandwichedRenyiEntropy_mono_traceLeft hα .. _ = D̃_ α(ρ ⊗ᴹ τ‖σ ⊗ᴹ τ) := sandwichedRenyiEntropy_conj_unitary (by positivity) _ _ _ _ = D̃_ α(ρ‖σ) := sandwichedRenyiEntropy_tensor_pure (by positivity) ρ σ ψ₀- Project
- quantumInfo
- License
- MIT
- Commit
- 56e83a9288a3
- Source
- QuantumInfo/Finite/Entropy/DPI.lean:1540-1569
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Related declarations
Conj Transpose isometry mul isometry le one
conjTranspose_isometry_mul_isometry_le_one
Project documentation
The operator norm of the conjugate transpose is equal to the operator norm. -/ theorem Matrix.opNorm_conjTranspose_eq_opNorm {m n : Type*} [Fintype m] [Fintype n] [DecidableEq m] [DecidableEq n] (A : Matrix m n 𝕜) : Matrix.opNorm Aᴴ = Matrix.opNorm A := by unfold Matrix.opNorm rw [← ContinuousLinearMap.adjoint.norm_map (toEuclideanLin A).toContinuousLine...
Source project: quantumInfo
Person-level attribution pending.
Convex roof of pure
convex_roof_of_pure
Plain-language statement
The convex roof extension of g : KetUpToPhase d → ℝ≥0 applied to a pure state ψ is g (KetUpToPhase.mk ψ).
Source project: quantumInfo
Person-level attribution pending.
Id achieves Rate log dim
CPTPMap.id_achievesRate_log_dim
Plain-language statement
The identity channel on D dimensional space achieves a rate of log2(D).
Source project: quantumInfo
Person-level attribution pending.