import OAI.Probability.EntangledGames.Resampling namespace OAI universe u_R u_n u_X u_Y u_I u_A u_B u_m u_J u_K noncomputable section open scoped BigOperators MatrixOrder ComplexOrder open Matrix namespace ThresholdParallelRepetition open QuantumSampling Resolvent OperatorEntropy FiniteProbability namespace FiniteProbability.Law variable {R : Type u_R} {n : Type u_n} [Fintype R] [Fintype n] [DecidableEq n] omit [Fintype n] [DecidableEq n] in lemma avg_posSemidef (p : Law R) (A : R → Matrix n n ℂ) (hA : ∀ r, (A r).PosSemidef) : (p.avg A).PosSemidef := by apply Matrix.posSemidef_sum intro r _ exact (hA r).smul (p.nonneg r) lemma avg_matrix_mono (p : Law R) {A B : R → Matrix n n ℂ} (h : ∀ r, A r ≤ B r) : p.avg A ≤ p.avg B := by unfold avg exact Finset.sum_le_sum (fun r _ => smul_le_smul_of_nonneg_left (h r) (p.nonneg r)) lemma avg_matrix_le (p : Law R) {A : R → Matrix n n ℂ} {B : Matrix n n ℂ} (h : ∀ r, A r ≤ B) : p.avg A ≤ B := by simpa only [avg_const] using p.avg_matrix_mono h variable {X : Type u_X} {Y : Type u_Y} {I : Type u_I} [Fintype X] [Fintype Y] [DecidableEq X] [DecidableEq Y] [DecidableEq I] omit [DecidableEq Y] [Fintype n] [DecidableEq n] in lemma lefts_posSemidef (μ : Law (X×Y)) (x₀ : X) (l : List I) (f : Profile I X Y → Matrix n n ℂ) (hf : ∀ z, (f z).PosSemidef) : ∀ z, (μ.lefts x₀ l f z).PosSemidef := by induction l with | nil => exact hf | cons i l ih => intro z; exact avg_posSemidef _ _ (fun _ => ih _) omit [DecidableEq X] [Fintype n] [DecidableEq n] in lemma rights_posSemidef (μ : Law (X×Y)) (y₀ : Y) (l : List I) (f : Profile I X Y → Matrix n n ℂ) (hf : ∀ z, (f z).PosSemidef) : ∀ z, (μ.rights y₀ l f z).PosSemidef := by induction l with | nil => exact hf | cons i l ih => intro z; exact avg_posSemidef _ _ (fun _ => ih _) omit [DecidableEq Y] in lemma lefts_le (μ : Law (X×Y)) (x₀ : X) (l : List I) (f : Profile I X Y → Matrix n n ℂ) {B : Matrix n n ℂ} (hf : ∀ z, f z ≤ B) : ∀ z, μ.lefts x₀ l f z ≤ B := by induction l with | nil => exact hf | cons i l ih => intro z; exact avg_matrix_le _ (fun _ => ih _) omit [DecidableEq X] in lemma rights_le (μ : Law (X×Y)) (y₀ : Y) (l : List I) (f : Profile I X Y → Matrix n n ℂ) {B : Matrix n n ℂ} (hf : ∀ z, f z ≤ B) : ∀ z, μ.rights y₀ l f z ≤ B := by induction l with | nil => exact hf | cons i l ih => intro z; exact avg_matrix_le _ (fun _ => ih _) end FiniteProbability.Law namespace QuantumSampling.POVM variable {n : Type u_n} {A : Type u_A} {B : Type u_B} {R : Type u_R} [Fintype n] [DecidableEq n] [Fintype A] [Fintype B] [Fintype R] lemma le_one (P : POVM n A) (a : A) : P.effect a ≤ 1 := by rw [← P.total] exact Finset.single_le_sum (fun b _ => (P.pos b).nonneg) (Finset.mem_univ a) def coarsen (P : POVM n A) (f : A → B) : POVM n B := by classical exact { effect b := ∑ a with f a=b, P.effect a pos b := Matrix.posSemidef_sum _ (fun a _ => P.pos a) total := by simp only [Finset.sum_fiberwise_of_maps_to (fun a _ => Finset.mem_univ (f a)), P.total] } lemma coarsen_sum (P : POVM n A) (f : A → B) (g : B → Matrix n n ℂ →ₗ[ℝ] ℝ) : (∑ b, g b ((P.coarsen f).effect b)) = ∑ a, g (f a) (P.effect a) := by classical simp only [coarsen, map_sum, Finset.sum_filter] rw [Finset.sum_comm] simp only [apply_ite, map_zero, Finset.sum_ite_eq, Finset.mem_univ, ite_true] def average (p : Law R) (P : R → POVM n A) : POVM n A where effect a := p.avg (fun r => (P r).effect a) pos a := p.avg_posSemidef _ (fun r => (P r).pos a) total := by simp only [Law.avg] rw [Finset.sum_comm] simp only [← Finset.smul_sum, (P _).total] exact p.avg_const 1 end QuantumSampling.POVM namespace OperatorEntropy variable {R : Type u_R} {m : Type u_m} {n : Type u_n} [Fintype R] [Fintype m] [Fintype n] [DecidableEq m] [DecidableEq n] lemma potential_left_lower (C : Matrix m n ℂ) (hC : hsSq C ≤ 1) (w : R → ℝ) (hw : ∀ r, 0 ≤ w r) (F : R → Matrix m m ℂ) (H : R → Matrix n n ℂ) (hF : ∀ r, (F r).PosSemidef) (hH : ∀ r, (H r).PosSemidef) (hH1 : ∀ r, H r ≤ 1) {d p : ℝ} (hd : 0 < d) (hs : ∑ r, w r ≤ d) (hp : 0 < p) (hm : ∑ r, w r*prob C (F r) (H r) = p) : -p*Real.log (d/p) ≤ ∑ r, w r * prob C (effectEntropy (F r)) (H r) := by have h := ScalarEntropy.weighted_negMulLog_le w (fun r => prob C (F r) (H r)) hw (fun r => prob_nonneg (hF r) (hH r)) hd hs hp hm have h' : -(∑ r, w r*prob C (effectEntropy (F r)) (H r)) ≤ ∑ r, w r*Real.negMulLog (prob C (F r) (H r)) := by rw [← Finset.sum_neg_distrib] exact Finset.sum_le_sum (fun r _ => by simpa only [mul_neg] using mul_le_mul_of_nonneg_left (prob_entropy_left_lower C hC (hF r) (hH r) (hH1 r)) (hw r)) linarith lemma potential_right_lower (C : Matrix m n ℂ) (hC : hsSq C ≤ 1) (w : R → ℝ) (hw : ∀ r, 0 ≤ w r) (F : R → Matrix m m ℂ) (H : R → Matrix n n ℂ) (hF : ∀ r, (F r).PosSemidef) (hF1 : ∀ r, F r ≤ 1) (hH : ∀ r, (H r).PosSemidef) {d p : ℝ} (hd : 0 < d) (hs : ∑ r, w r ≤ d) (hp : 0 < p) (hm : ∑ r, w r*prob C (F r) (H r) = p) : -p*Real.log (d/p) ≤ ∑ r, w r * prob C (F r) (effectEntropy (H r)) := by have h := ScalarEntropy.weighted_negMulLog_le w (fun r => prob C (F r) (H r)) hw (fun r => prob_nonneg (hF r) (hH r)) hd hs hp hm have h' : -(∑ r, w r*prob C (F r) (effectEntropy (H r))) ≤ ∑ r, w r*Real.negMulLog (prob C (F r) (H r)) := by rw [← Finset.sum_neg_distrib] exact Finset.sum_le_sum (fun r _ => by simpa only [mul_neg] using mul_le_mul_of_nonneg_left (prob_entropy_right_lower C hC (hF r) (hF1 r) (hH r)) (hw r)) linarith omit [DecidableEq n] in lemma potential_left_nonpos (C : Matrix m n ℂ) (w : R → ℝ) (hw : ∀ r, 0 ≤ w r) (F : R → Matrix m m ℂ) (H : R → Matrix n n ℂ) (hF : ∀ r, (F r).PosSemidef) (hF1 : ∀ r, F r ≤ 1) (hH : ∀ r, (H r).PosSemidef) : (∑ r, w r * prob C (effectEntropy (F r)) (H r)) ≤ 0 := by apply Finset.sum_nonpos intro r _ apply mul_nonpos_of_nonneg_of_nonpos (hw r) simpa only [prob, eval_eq_trace, Matrix.mul_zero, Matrix.zero_mul, Matrix.trace_zero, Complex.zero_re] using prob_mono_left C (hH r) (effectEntropy_nonpos (hF r) (hF1 r)) omit [DecidableEq m] in lemma potential_right_nonpos (C : Matrix m n ℂ) (w : R → ℝ) (hw : ∀ r, 0 ≤ w r) (F : R → Matrix m m ℂ) (H : R → Matrix n n ℂ) (hF : ∀ r, (F r).PosSemidef) (hH : ∀ r, (H r).PosSemidef) (hH1 : ∀ r, H r ≤ 1) : (∑ r, w r * prob C (F r) (effectEntropy (H r))) ≤ 0 := by apply Finset.sum_nonpos intro r _ apply mul_nonpos_of_nonneg_of_nonpos (hw r) simpa only [prob, eval_eq_trace, Matrix.transpose_zero, Matrix.mul_zero, Matrix.trace_zero, Complex.zero_re] using prob_mono_right C (hF r) (effectEntropy_nonpos (hH r) (hH1 r)) end OperatorEntropy namespace Resolvent variable {J : Type u_J} {n : Type u_n} [Fintype J] [Nonempty J] [DecidableEq J] [Fintype n] [DecidableEq n] def effectIndex (A : J → Matrix n n ℂ) (T : Matrix n n ℂ) : J := if h : T ∈ Set.range A then Classical.choose h else Classical.choice inferInstance def commonMap (A : J → Matrix n n ℂ) (T : Matrix n n ℂ) : Matrix (J×n) n ℂ := finitePurification A (effectIndex A T) omit [DecidableEq J] [DecidableEq n] in lemma effectIndex_eq (A : J → Matrix n n ℂ) {T : Matrix n n ℂ} (hT : T ∈ Set.range A) : A (effectIndex A T) = T := by simp only [effectIndex, dite_eq_left hT] exact Classical.choose_spec hT lemma commonMap_gram (A : J → Matrix n n ℂ) (hA : ∀ j, (A j).PosSemidef) {T : Matrix n n ℂ} (hT : T ∈ Set.range A) : (commonMap A T)ᴴ*commonMap A T = T := by rw [commonMap, finitePurification_self A hA, effectIndex_eq A hT] lemma commonMap_jensen {R : Type u_R} [Fintype R] (A : J → Matrix n n ℂ) (hA : ∀ j, (A j).PosSemidef) (hA1 : ∀ j, A j ≤ 1) (p : Law R) (F : R → Matrix n n ℂ) (hF : ∀ r, F r ∈ Set.range A) (hmean : p.avg F ∈ Set.range A) : p.avg (fun r => (commonMap A (F r)-commonMap A (p.avg F))ᴴ * (commonMap A (F r)-commonMap A (p.avg F))) ≤ p.avg (fun r => effectEntropy (F r)) - effectEntropy (p.avg F) := by have h := finitePurification_jensen A hA hA1 (fun r => effectIndex A (F r)) p.weight (effectIndex A (p.avg F)) p.nonneg p.total (by simp only [effectIndex_eq A (hF _), effectIndex_eq A hmean]; rfl) simp only [effectIndex_eq A (hF _), effectIndex_eq A hmean] at h exact h variable {m : Type u_m} {K : Type u_K} [Fintype m] [DecidableEq m] [Fintype K] [DecidableEq K] omit [DecidableEq m] in lemma commonMap_error_left {R : Type u_R} [Fintype R] (A : J → Matrix n n ℂ) (hA : ∀ j, (A j).PosSemidef) (hA1 : ∀ j, A j ≤ 1) (p : Law R) (F : R → Matrix n n ℂ) (hF : ∀ r, F r ∈ Set.range A) (hmean : p.avg F ∈ Set.range A) (C : Matrix n m ℂ) (L : Matrix K m ℂ) : p.avg (fun r => hsSq ((commonMap A (F r)-commonMap A (p.avg F))*C*Lᵀ)) ≤ p.avg (fun r => prob C (effectEntropy (F r)) (Lᴴ*L)) - prob C (effectEntropy (p.avg F)) (Lᴴ*L) := by have h := prob_mono_left C (Matrix.posSemidef_conjTranspose_mul_self L) (commonMap_jensen A hA hA1 p F hF hmean) rw [prob_sub_left] at h have he (T : R → Matrix n n ℂ) : prob C (p.avg T) (Lᴴ*L) = p.avg (fun r => prob C (T r) (Lᴴ*L)) := p.avg_linear T (probLeft C (Lᴴ*L)) rw [he, he] at h convert h using 1 apply congrArg p.avg funext r exact (prob_gram C _ L).symm omit [DecidableEq m] in lemma commonMap_error_right {R : Type u_R} [Fintype R] (A : J → Matrix n n ℂ) (hA : ∀ j, (A j).PosSemidef) (hA1 : ∀ j, A j ≤ 1) (p : Law R) (F : R → Matrix n n ℂ) (hF : ∀ r, F r ∈ Set.range A) (hmean : p.avg F ∈ Set.range A) (C : Matrix m n ℂ) (L : Matrix K m ℂ) : p.avg (fun r => hsSq (L*C*(commonMap A (F r)-commonMap A (p.avg F))ᵀ)) ≤ p.avg (fun r => prob C (Lᴴ*L) (effectEntropy (F r))) - prob C (Lᴴ*L) (effectEntropy (p.avg F)) := by have h := prob_mono_right C (Matrix.posSemidef_conjTranspose_mul_self L) (commonMap_jensen A hA hA1 p F hF hmean) rw [prob_sub_right] at h have he (T : R → Matrix n n ℂ) : prob C (Lᴴ*L) (p.avg T) = p.avg (fun r => prob C (Lᴴ*L) (T r)) := p.avg_linear T (probRight C (Lᴴ*L)) rw [he, he] at h convert h using 1 apply congrArg p.avg funext r exact (prob_gram C L _).symm end Resolvent end ThresholdParallelRepetition end end OAI