state_metrics.bures_distance

Bures distance metric.

Module Contents

Functions

bures_distance(rho_1, rho_2[, decimals])

Compute the Bures distance of two density matrices [1].

state_metrics.bures_distance.bures_distance(rho_1, rho_2, decimals=10)

Compute the Bures distance of two density matrices [1].

Calculate the Bures distance between two density matrices rho_1 and rho_2 defined by:

\[\sqrt{2 (1 - F(\rho_1, \rho_2))},\]

where \(F(\cdot)\) denotes the fidelity between \(\rho_1\) and \(\rho_2\). The return is a value between \(0\) and \(\sqrt{2}\),with \(0\) corresponding to matrices: rho_1 = rho_2 and \(\sqrt{2}\) corresponding to the case: rho_1 and rho_2 with orthogonal support.

Examples

Consider the following Bell state

\[u = \frac{1}{\sqrt{2}} \left( |00 \rangle + |11 \rangle \right) \in \mathcal{X}.\]

The corresponding density matrix of \(u\) may be calculated by:

\[\begin{split}\rho = u u^* = \frac{1}{2} \begin{pmatrix} 1 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \\ 1 & 0 & 0 & 1 \end{pmatrix} \in \text{D}(\mathcal{X}).\end{split}\]

In the event where we calculate the Bures distance between states that are identical, we should obtain the value of \(0\). This can be observed in toqito as follows.

>>> from toqito.state_metrics import bures_distance
>>> import numpy as np
>>> rho = 1 / 2 * np.array(
...     [[1, 0, 0, 1],
...      [0, 0, 0, 0],
...      [0, 0, 0, 0],
...      [1, 0, 0, 1]]
... )
>>> sigma = rho
>>> bures_distance(rho, sigma)
0.0

References

[1] (1,2)

Wikipedia. Bures distance. https://en.wikipedia.org/wiki/Bures_metric#Bures_distance.

Raises:

ValueError – If matrices are not of equal dimension.

Parameters:

• rho_1 (numpy.ndarray) – Density operator.

• rho_2 (numpy.ndarray) – Density operator.

• decimals (int) – Number of decimal places to round to (default 10).

Returns:

The Bures distance between rho_1 and rho_2.

Return type:

float