# Difference between revisions of "DiamondNorm"

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|name=DiamondNorm | |name=DiamondNorm | ||

|desc=Computes the [[diamond norm]] of a superoperator | |desc=Computes the [[diamond norm]] of a superoperator | ||

− | |req=[http://cvxr.com/cvx/ | + | |req=[http://cvxr.com/cvx/ CVX] |

|rel=[[CBNorm]]<br />[[InducedSchattenNorm]]<br />[[MaximumOutputFidelity]] | |rel=[[CBNorm]]<br />[[InducedSchattenNorm]]<br />[[MaximumOutputFidelity]] | ||

|cat=[[List of functions#Norms|Norms]] | |cat=[[List of functions#Norms|Norms]] |

## Latest revision as of 16:40, 13 June 2018

DiamondNorm | |

Computes the diamond norm of a superoperator | |

Other toolboxes required | CVX |
---|---|

Related functions | CBNorm InducedSchattenNorm MaximumOutputFidelity |

Function category | Norms |

Usable within CVX? | yes (convex) |

` DiamondNorm` is a function that computes the diamond norm $\|\Phi\|_\diamond$ of a superoperator $\Phi$.

## Syntax

`DN = DiamondNorm(PHI)``DN = DiamondNorm(PHI,DIM)`

## Argument descriptions

`PHI`: A superoperator. Should be provided as either a Choi matrix, or as a cell with either 1 or 2 columns (see the tutorial page for more details about specifying superoperators within QETLAB).`PHIC`will be a cell of Kraus operators if`PHI`is a cell of Kraus operators, and similarly`PHIC`will be a Choi matrix if`PHI`is a Choi matrix.`DIM`(optional, default has input and output spaces of equal dimension): A 1-by-2 vector containing the input and output dimensions of`PHI`, in that order (equivalently, these are the dimensions of the first and second subsystems of the Choi matrix`PHI`, in that order). If the input or output space is not square, then`DIM`'s first row should contain the input and output row dimensions, and its second row should contain its input and output column dimensions.`DIM`is required if and only if`PHI`has unequal input and output dimensions and is provided as a Choi matrix.

## Examples

### A completely positive map

If $\Phi$ is completely positive then $\|\Phi\|_{\diamond} = \|\Phi^\dagger(I)\|$, where $I$ is the identity matrix, $\Phi^\dagger$ is the dual map of $\Phi$, and $\|\cdot\|$ is the usual operator norm, which we can verify in a special case via the following code:

```
>> Phi = {[1 2;3 4] ; [0 1;2 0] ; [1 1;-1 3]};
>> DiamondNorm(Phi)
ans =
37.6510
>> norm(ApplyMap(eye(2),DualMap(Phi)))
ans =
37.6510
```

### A difference of unitaries channel

If $\Phi(X) = X - UXU^\dagger$, then the diamond norm of $\Phi$ is the diameter of the smallest circle that contains the eigenvalues of $U$, which we can verify in a special case via the following code:

```
>> U = [1 1;-1 1]/sqrt(2);
>> Phi = {eye(2),eye(2); U,-U};
>> DiamondNorm(Phi)
ans =
1.4142
>> lam = eig(U)
lam =
0.7071 + 0.7071i
0.7071 - 0.7071i
>> abs(lam(1) - lam(2))
ans =
1.4142
```

### Can be used within CVX

The diamond norm is convex (like all norms) and this function can be used in the same way as any other convex function within CVX. Thus you can minimize the diamond norm or use the diamond norm in constraints of CVX optimization problems. For example, the following code finds the closest Pauli channel (i.e., channel with Kraus operators all of which are multiples of Pauli matrices) to a given 2-qubit channel, where the measure of "closest" is the diamond norm:

```
>> num_qubits = 2;
>> H = [1,1;1,-1]/sqrt(2); % Hadamard gate
>> Psi = ChoiMatrix({Tensor(H,num_qubits)}); % channel that acts as Hadamard gate on 2 qubits
>> cvx_begin sdp quiet
variable Phi(4^num_qubits,4^num_qubits) hermitian;
variable p(4^num_qubits);
minimize DiamondNorm(Phi - Psi)
subject to
% these constraints force Phi to be a Pauli channel
Phi == PauliChannel(p);
sum(p) == 1;
p >= 0;
cvx_end
cvx_optval
cvx_optval =
1.5000
```

## Source code

Click on "expand" to the right to view the MATLAB source code for this function.