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QuantumInfo.ForMathlib.MatrixNorm.TraceNorm

def Matrix.traceNorm {m : Type u_1} {n : Type u_2} {R : Type u_3} [Fintype m] [Fintype n] [RCLike R] (A : Matrix m n R) :

The trace norm of a matrix: Tr[√(A† A)].

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Instances For
    @[simp]
    theorem Matrix.traceNorm_eq_neg_self {m : Type u_1} {n : Type u_2} {R : Type u_3} [Fintype m] [Fintype n] [RCLike R] (A : Matrix m n R) :

    The trace norm of the negative is equal to the trace norm.

    theorem Matrix.traceNorm_Hermitian_eq_sum_abs_eigenvalues {n : Type u_2} {R : Type u_3} [Fintype n] [RCLike R] {A : Matrix n n R} (hA : A.IsHermitian) :
    A.traceNorm = i : n, |hA.eigenvalues i|
    theorem Matrix.traceNorm_nonneg {m : Type u_1} {n : Type u_2} {R : Type u_3} [Fintype m] [Fintype n] [RCLike R] (A : Matrix m n R) :

    The trace norm is nonnegative. Property 9.1.1 in Wilde

    theorem Matrix.traceNorm_zero_iff {m : Type u_1} {n : Type u_2} {R : Type u_3} [Fintype m] [Fintype n] [RCLike R] (A : Matrix m n R) :
    A.traceNorm = 0 A = 0

    The trace norm is zero only if the matrix is zero.

    theorem Matrix.traceNorm_smul {m : Type u_1} {n : Type u_2} {R : Type u_3} [Fintype m] [Fintype n] [RCLike R] (A : Matrix m n R) (c : R) :

    Trace norm is linear under scalar multiplication. Property 9.1.2 in Wilde

    theorem Matrix.traceNorm_eq_max_tr_U {n : Type u_2} {R : Type u_3} [Fintype n] [RCLike R] (A : Matrix n n R) :
    IsGreatest {x : R | ∃ (U : (unitaryGroup n R)), (U * A).trace = x} A.traceNorm

    For square matrices, the trace norm is max Tr[U * A] over unitaries U.

    theorem Matrix.traceNorm_triangleIneq {n : Type u_2} {R : Type u_3} [Fintype n] [RCLike R] (A B : Matrix n n R) :

    the trace norm satisfies the triangle inequality (for square matrices). TODO: Prove in general.

    theorem Matrix.traceNorm_triangleIneq' {n : Type u_2} {R : Type u_3} [Fintype n] [RCLike R] (A B : Matrix n n R) :
    theorem Matrix.PosSemidef.traceNorm_PSD_eq_trace {m : Type u_1} {R : Type u_3} [Fintype m] [RCLike R] {A : Matrix m m R} (hA : A.PosSemidef) :