measurement_props.is_povm¶
Determine if a list of matrices are POVM elements.
Functions¶
Module Contents¶
- measurement_props.is_povm.is_povm(mat_list)¶
Determine if a list of matrices constitute a valid set of POVMs [1].
A valid set of measurements are defined by a set of positive semidefinite operators
\[\{P_a : a \in \Gamma\} \subset \text{Pos}(\mathcal{X}),\]indexed by the alphabet \(\Gamma\) of measurement outcomes satisfying the constraint that
\[\sum_{a \in \Gamma} P_a = I_{\mathcal{X}}.\]Examples
Consider the following matrices:
\[\begin{split}M_0 = \begin{pmatrix} 1 & 0 \\ 0 & 0 \end{pmatrix} \quad \text{and} \quad M_1 = \begin{pmatrix} 0 & 0 \\ 0 & 1 \end{pmatrix}.\end{split}\]Our function indicates that this set of operators constitute a set of POVMs.
import numpy as np from toqito.measurement_props import is_povm meas_1 = np.array([[1, 0], [0, 0]]) meas_2 = np.array([[0, 0], [0, 1]]) meas = [meas_1, meas_2] is_povm(meas)
True
We may also use the
random_povm
function from|toqito⟩
, and can verify that a randomly generated set satisfies the criteria for being a POVM set.import numpy as np from toqito.rand import random_povm from toqito.measurement_props import is_povm dim, num_inputs, num_outputs = 2, 2, 2 measurements = random_povm(dim, num_inputs, num_outputs) is_povm([measurements[:, :, 0, 0], measurements[:, :, 0, 1]])
True
Alternatively, the following matrices
\[\begin{split}M_0 = \begin{pmatrix} 1 & 2 \\ 3 & 4 \end{pmatrix} \quad \text{and} \quad M_1 = \begin{pmatrix} 5 & 6 \\ 7 & 8 \end{pmatrix},\end{split}\]do not constitute a POVM set.
import numpy as np from toqito.measurement_props import is_povm non_meas_1 = np.array([[1, 2], [3, 4]]) non_meas_2 = np.array([[5, 6], [7, 8]]) non_meas = [non_meas_1, non_meas_2] is_povm(non_meas)
False
References
- Parameters:
mat_list (list[numpy.ndarray]) – A list of matrices.
- Returns:
Return
True
if set of matrices constitutes a set of measurements, andFalse
otherwise.- Return type:
bool