The hermitian optimized eigen solver function in scipy has shown
to possibly produce varied results in different environments. For
example, the matrix returned by the eigh() call in the
TwoQubitWeylDecomposition class's __init__ method when called for
a CXGate:

TwoQubitWeylDecomposition(Operator(CXGate()))

was observed to be

[[ 0.70710678  0.         -0.70710678  0.        ]
 [ 0.         -0.70710678  0.          0.70710678]
 [ 0.          0.70710678  0.          0.70710678]
 [ 0.70710678  0.          0.70710678  0.        ]]

on one system/environment but

[[ 0.70710678  0.         -0.70710678  0.        ]
 [ 0.          0.70710678  0.          0.70710678]
 [ 0.         -0.70710678  0.          0.70710678]
 [ 0.70710678  0.          0.70710678  0.        ]]

on a different environment. This difference was resulting in
inconsistent decompositions being generated and made reproducing results
from the transpiler difficult. In testing the numpy equivalent
function [2] has proven to be more reliable across different
environments. This commit switches the eigh() usage in the
TwoQubitWeylDecomposition class to use the numpy function instead
this should fix the inconsistent decompositions we were seeing between
environments.

[1] https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eigh.html
[2] https://numpy.org/doc/stable/reference/generated/numpy.linalg.eigh.html

This commit removes a test Qiskit#4835 which was added to validate that the
internal values for the decomposer didn't sign flip in the specific case
that the PR was fixing. However with the use of the numpy eigh()
function these internal values no longer the same. So instead of
updating the test this just deletes it since the original case it was
catching is no longer applicable.

This commit reverts back to using the scipy eigh() function but sets a
the lapack driver to be fixed as evd. The numpy routine was using the
evd driver while scipy would default to use ev as the driver. Switching
the scipy driver from ev to evd seems to be the key difference between
the routines which was causing the inconsistent results with scipy.
Approaching it this way means we avoid need to bump the minimum numpy
version in the requirements list since the numpy eigh() function was
added in a fairly recent version.