This fixes several places in `quantum_info` that will be affected by the
casting-rule changes coming in Numpy 2.0[^1].  There are several other
places where the casting rules will change the output types of certain
arrays due to the removal of Python-scalar value-dependent casting, but
those do not appear to have an observable effect on our behaviour, so I
have left them.

It is also becoming invalid to set a scalar entry in a Numpy array with
an array-like with a non-zero number of dimensions, even if the sequence
is of length 1.

[^1]: https://numpy.org/neps/nep-0050-scalar-promotion.html

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- Qiskitgh-10890
- Qiskitgh-10891
- Qiskitgh-10892
- Qiskitgh-10897
- Qiskitgh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- Qiskitgh-10890
- Qiskitgh-10891
- Qiskitgh-10892
- Qiskitgh-10897
- Qiskitgh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

The main changes are:

- adapting to the changed `copy=None` and `copy=False` semantics in
  `array` and `asarray`.
- making sure all our implementers of `__array__` accept both `dtype`
  and `copy` arguments.

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- Qiskitgh-10890
- Qiskitgh-10891
- Qiskitgh-10892
- Qiskitgh-10897
- Qiskitgh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

The main changes are:

- adapting to the changed `copy=None` and `copy=False` semantics in
  `array` and `asarray`.
- making sure all our implementers of `__array__` accept both `dtype`
  and `copy` arguments.

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>

* Finalise support for Numpy 2.0

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- gh-10890
- gh-10891
- gh-10892
- gh-10897
- gh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

The main changes are:

- adapting to the changed `copy=None` and `copy=False` semantics in
  `array` and `asarray`.
- making sure all our implementers of `__array__` accept both `dtype`
  and `copy` arguments.

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>

* Update `__array__` methods for Numpy 2.0 compatibility

As of Numpy 2.0, implementers of `__array__` are expected and required
to have a signature

    def __array__(self, dtype=None, copy=None): ...

In Numpys before 2.0, the `copy` argument will never be passed, and the
expected signature was

    def __array__(self, dtype=None): ...

Because of this, we have latitude to set `copy` in our implementations
to anything we like if we're running against Numpy 1.x, but we should
default to `copy=None` if we're running against Numpy 2.0.

The semantics of the `copy` argument to `np.array` changed in Numpy 2.0.
Now, `copy=False` means "raise a `ValueError` if a copy is required" and
`copy=None` means "copy only if required".  In Numpy 1.x, `copy=False`
meant "copy only if required".  In _both_ Numpy 1.x and 2.0,
`ndarray.astype` takes a `copy` argument, and in both, `copy=False`
means "copy only if required".  In Numpy 2.0 only, `np.asarray` gained a
`copy` argument with the same semantics as the `np.array` copy argument
from Numpy 2.0.

Further, the semantics of the `__array__` method changed in Numpy 2.0,
particularly around copying.  Now, Numpy will assume that it can pass
`copy=True` and the implementer will handle this.  If `copy=False` is
given and a copy or calculation is required, then the implementer is
required to raise `ValueError`.  We have a few places where the
`__array__` method may (or always does) calculate the array, so in all
these, we must forbid `copy=False`.

With all this in mind: this PR sets up all our implementers of
`__array__` to either default to `copy=None` if they will never actually
need to _use_ the `copy` argument within themselves (except perhaps to
test if it was set by Numpy 2.0 to `False`, as Numpy 1.x will never set
it), or to a compatibility shim `_numpy_compat.COPY_ONLY_IF_NEEDED` if
they do naturally want to use it with those semantics.  The pattern

    def __array__(self, dtype=None, copy=_numpy_compat.COPY_ONLY_IF_NEEDED):
        dtype = self._array.dtype if dtype is None else dtype
        return np.array(self._array, dtype=dtype, copy=copy)

using `array` instead of `asarray` lets us achieve all the desired
behaviour between the interactions of `dtype` and `copy` in a way that
is compatible with both Numpy 1.x and 2.x.

* fixing numerical issues on mac-arm

* Change error to match Numpy

---------

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>
Co-authored-by: Sebastian Brandhofer <148463728+sbrandhsn@users.noreply.github.com>

* Finalise support for Numpy 2.0

This commit brings the Qiskit test suite to a passing state (with all
optionals installed) with Numpy 2.0.0b1, building on previous commits
that handled much of the rest of the changing requirements:

- gh-10890
- gh-10891
- gh-10892
- gh-10897
- gh-11023

Notably, this commit did not actually require a rebuild of Qiskit,
despite us compiling against Numpy; it seems to happen that the C API
stuff we use via `rust-numpy` (which loads the Numpy C extensions
dynamically during module initialisation) hasn't changed.

The main changes are:

- adapting to the changed `copy=None` and `copy=False` semantics in
  `array` and `asarray`.
- making sure all our implementers of `__array__` accept both `dtype`
  and `copy` arguments.

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>

* Update `__array__` methods for Numpy 2.0 compatibility

As of Numpy 2.0, implementers of `__array__` are expected and required
to have a signature

    def __array__(self, dtype=None, copy=None): ...

In Numpys before 2.0, the `copy` argument will never be passed, and the
expected signature was

    def __array__(self, dtype=None): ...

Because of this, we have latitude to set `copy` in our implementations
to anything we like if we're running against Numpy 1.x, but we should
default to `copy=None` if we're running against Numpy 2.0.

The semantics of the `copy` argument to `np.array` changed in Numpy 2.0.
Now, `copy=False` means "raise a `ValueError` if a copy is required" and
`copy=None` means "copy only if required".  In Numpy 1.x, `copy=False`
meant "copy only if required".  In _both_ Numpy 1.x and 2.0,
`ndarray.astype` takes a `copy` argument, and in both, `copy=False`
means "copy only if required".  In Numpy 2.0 only, `np.asarray` gained a
`copy` argument with the same semantics as the `np.array` copy argument
from Numpy 2.0.

Further, the semantics of the `__array__` method changed in Numpy 2.0,
particularly around copying.  Now, Numpy will assume that it can pass
`copy=True` and the implementer will handle this.  If `copy=False` is
given and a copy or calculation is required, then the implementer is
required to raise `ValueError`.  We have a few places where the
`__array__` method may (or always does) calculate the array, so in all
these, we must forbid `copy=False`.

With all this in mind: this PR sets up all our implementers of
`__array__` to either default to `copy=None` if they will never actually
need to _use_ the `copy` argument within themselves (except perhaps to
test if it was set by Numpy 2.0 to `False`, as Numpy 1.x will never set
it), or to a compatibility shim `_numpy_compat.COPY_ONLY_IF_NEEDED` if
they do naturally want to use it with those semantics.  The pattern

    def __array__(self, dtype=None, copy=_numpy_compat.COPY_ONLY_IF_NEEDED):
        dtype = self._array.dtype if dtype is None else dtype
        return np.array(self._array, dtype=dtype, copy=copy)

using `array` instead of `asarray` lets us achieve all the desired
behaviour between the interactions of `dtype` and `copy` in a way that
is compatible with both Numpy 1.x and 2.x.

---------

Co-authored-by: Lev S. Bishop <18673315+levbishop@users.noreply.github.com>
Co-authored-by: Raynel Sanchez <87539502+raynelfss@users.noreply.github.com>