InducedSchattenNorm

 Other toolboxes required InducedSchattenNorm Computes a lower bound of the induced p→q Schatten norm of a superoperator none DiamondNormInducedMatrixNormSchattenNorm Norms no

InducedSchattenNorm is a function that computes a randomized lower bound of the induced p→q Schatten norm of a superoperator, defined as follows [1]: $\|\Phi\|_{p\rightarrow q} := \max\big\{\|\Phi(X)\|_q : \|X\|_p = 1 \big\},$ where $\|X\|_{p} := \left(\sum_i\sigma_i(X)^p\right)^{1/p}$ is the Schatten p-norm.

When p = q = 1, this is the induced trace norm that comes up frequently in quantum information theory (and whose stabilization is the diamond norm). In the p = q = Inf case, this is usually called the operator norm of $$\Phi$$, which comes up frequently in operator theory.

The lower bound is found via the algorithm described here, which starts with a random input matrix and performs a local optimization based on that starting matrix.

Syntax

• NRM = InducedSchattenNorm(PHI,P)
• NRM = InducedSchattenNorm(PHI,P,Q)
• NRM = InducedSchattenNorm(PHI,P,Q,DIM)
• NRM = InducedSchattenNorm(PHI,P,Q,DIM,TOL)
• NRM = InducedSchattenNorm(PHI,P,Q,DIM,TOL,X0)
• [NRM,X] = InducedSchattenNorm(PHI,P,Q,DIM,TOL,X0)

Argument descriptions

Input arguments

• PHI: A superoperator to have its induced Schatten (PQ)-norm computed, specified as either a Choi matrix or a cell array of Kraus operators.
• P: A real number ≥ 1, or Inf.
• Q (optional, default equals P): A real number ≥ 1, or Inf.
• DIM (optional): A 1-by-2 vector containing the input and output dimensions of PHI, in that order. Not required if PHI's input and output spaces have the same dimension or if it is provided as a cell array of Kraus operators.
• TOL (optional, default equals sqrt(eps)): Numerical tolerance used throughout the script.
• X0 (optional, default is randomly-generated): An input matrix to start the numerical search from.

Output arguments

• NRM: A lower bound on the norm of X.
• X (optional): A matrix with SchattenNorm(X,P) = 1 such that SchattenNorm(ApplyMap(X,PHI),Q) = NRM (i.e., an input matrix that attains the local maximum that was found).

Examples

A difference of unitaries channel

If $$\Phi(X) = X - UXU^\dagger$$, then the induced trace norm (i.e., Schatten 1-norm) of $$\Phi$$ is the diameter of the smallest circle that contains the eigenvalues of $$U$$. The following code verifies that this is indeed a lower bound in one special case:

>> U = [1 1;-1 1]/sqrt(2);
>> Phi = {eye(2),eye(2); U,-U};
>> InducedSchattenNorm(Phi,1)

ans =

1.4142

>> lam = eig(U)

lam =

0.7071 + 0.7071i
0.7071 - 0.7071i

>> abs(lam(1) - lam(2))

ans =

1.4142