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Project-declaredLean 4.28.0

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...

quantum informationentropyquantum channels

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Project-declaredLean 4.28.0

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).

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

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).

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

Not achieves Rate gt log dim out

CPTPMap.not_achievesRate_gt_log_dim_out

Plain-language statement

A channel cannot achieve a rate greater than log2(D), where D is the output dimension.

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

Doubly stochastic holder

doubly_stochastic_holder

Plain-language statement

Doubly stochastic Hölder inequality: for nonneg a, b, doubly stochastic w, and conjugate p, q > 1: ∑{ij} a_i * b_j * w{ij} ≤ (∑ a_i^p)^{1/p} * (∑ b_j^q)^{1/q}.

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

Mix m Ensemble pure average

Ensemble.mix_mEnsemble_pure_average

Plain-language statement

The average of f : MState d → T on an ensemble that mixes to a pure state ψ is f (pure ψ)

quantum informationentropyquantum channels

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Project-declaredLean 4.28.0

Mix p Ensemble pure average

Ensemble.mix_pEnsemble_pure_average

Plain-language statement

The average of f : Ket d → T on an ensemble that mixes to a pure state ψ is f ψ

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

Mix p Ensemble pure iff pure

Ensemble.mix_pEnsemble_pure_iff_pure

Project documentation

The average of f : MState d → T on a coerced pure-state ensemble ↑e : MEnsemble d α is equal to averaging the restricted function over Kets f ∘ pure : Ket d → T on e. -/ theorem average_of_pure_ensemble {T : Type _} {U : Type*} [AddCommGroup U] [Module ℝ U] [inst : Mixable U T] (f : MState d → T) (e : PEnsemble d α) : average f (toMEnsemble e) = p...

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

Eo F of MES

EoF_of_MES

Plain-language statement

The entanglement of formation of the maximally entangled state with on-site dimension 𝕕 is log(𝕕).

quantum informationentropyquantum channels

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Project-declaredLean 4.28.0

F alpha at optimizer

f_alpha_at_optimizer

Project documentation

Sub-lemma for Step 1b: the conj of H_hat by σ^{−γ} simplifies to (ρ.M.conj (σ^γ).mat)^{α−1}. This uses σ^{−γ} · σ^γ = identity (on support) to cancel the outer σ^γ factors. -/ theorem H_hat_conj_sigma (hα : 1 < α) (ρ σ : MState d) : let γ := (1 - α) / (2 * α) (H_hat α ρ σ).conj (σ.M ^ (-γ)).mat = (ρ.M.conj (σ.M ^ γ).mat) ^ (α - 1) := by intro γ have hγ :...

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

F alpha convex in sigma

f_alpha_convex_in_sigma

Plain-language statement

Step 3 (Convexity in σ): For fixed H ≥ 0 and ρ, and α > 1, the map σ ↦ f_alpha α H ρ σ is convex. The key is that for p = α/(α−1) > 1: • A ↦ Tr[A^p] is convex on PSD matrices (trace function convexity, Theorem 2.10 of Carlen), • σ ↦ σ^{−γ} H σ^{−γ} is concave in σ by Lieb concavity (since −γ = (α−1)/(2α) ∈ (0,½)), • The composition...

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

F alpha jointly convex

f_alpha_jointly_convex

Plain-language statement

Step 3 (Convexity in σ): For fixed H ≥ 0 and ρ, and α > 1, the map σ ↦ f_alpha α H ρ σ is convex. The key is that for p = α/(α−1) > 1: • A ↦ Tr[A^p] is convex on PSD matrices (trace function convexity, Theorem 2.10 of Carlen), • σ ↦ σ^{−γ} H σ^{−γ} is concave in σ by Lieb concavity (since −γ = (α−1)/(2α) ∈ (0,½)), • The composition...

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

F alpha le at optimizer

f_alpha_le_at_optimizer

Plain-language statement

Step 1c: H_hat is a maximizer: for all H ≥ 0, f_α(H) ≤ f_α(H_hat). This uses the trace Young inequality: for PSD A, B and conjugate exponents p, q > 1, ⟪A, B⟫ ≤ Tr[A^p]/p + Tr[B^q]/q. Applied with A = σ^γ ρ σ^γ, B = σ^{-γ} H σ^{-γ}, p = α, q = α/(α-1), the inner product identity ⟪ρ, H⟫ = ⟪A, B⟫ (under the support condition) yield...

quantum informationentropyquantum channels

Source project: quantumInfo

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Project-declaredLean 4.28.0

H hat conj sigma

H_hat_conj_sigma

Project documentation

Sub-lemma for Step 1b: the conj of H_hat by σ^{−γ} simplifies to (ρ.M.conj (σ^γ).mat)^{α−1}. This uses σ^{−γ} · σ^γ = identity (on support) to cancel the outer σ^γ factors.

quantum informationentropyquantum channels

Source project: quantumInfo

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