# target

`Graph.target(operator, filter_function_projector=None)`

Create information about the target for system time evolution.

Nodes created with this function contain two types of information: the target gate for the system time evolution, and the projection operator that defines the subspace of interest for robustness.

### Parameters

**operator**(*np.ndarray**or**Tensor*) – The target gate $U_\mathrm{target}$**filter_function_projector**(*np.ndarray**or**None**,**optional*) – The orthogonal projection matrix $P$

### Returns

The node containing the specified target information.

### Return type

### SEE ALSO

`Graph.infidelity_pwc`

: Total infidelity of a system with a piecewise-constant Hamiltonian.

`Graph.infidelity_stf`

: Total infidelity of a system with a sampleable Hamiltonian.

## Notes

The target gate $U_\mathrm{target}$ is a non-zero partial isometry, which means that it can be expressed in the form $\sum_j \left|\psi_j\right>\left<\phi_j\right|$, where $\left\{\left|\psi_j\right>\right\}$ and $\left\{\left|\phi_j\right>\right\}$ both form (non-empty) orthonormal, but not necessarily complete, sets. Such a target represents a target state $\left|\psi_j\right>$ for each initial state $\left|\phi_j\right>$. The resulting operational infidelity is 0 if and only if, up to global phase, each initial state $\left|\phi_j\right>$ maps exactly to the corresponding final state $\left|\psi_j\right>$

The filter function projector $P$ is an orthogonal projection matrix, which means that it satisfies $P=P^\dagger=P^2$. The image of $P$

## Examples

Define a target operation for the Pauli $X$

```
>>> target_operation = graph.target(operator=np.array([[0, 1], [1, 0]]))
>>> target_operation
<Target: operation_name="target", value_shape=(2, 2)>
```

See more examples in the How to optimize controls robust to strong noise sources user guide.