Merge remote-tracking branch 'upstream/master' into bath_pr_refactor2

doc/changes/2545.feature

@@ -0,0 +1 @@
+Implements a numpy.einsum version for Qobj dimensions (Evaluates the Einstein summation convention on the operands.)

doc/changes/2557.misc

@@ -0,0 +1 @@
+Improve performance of qt.Qobj by using static numpy version check 

qutip/core/data/dense.pyx

@@ -37,10 +37,11 @@ __all__ = [
 class OrderEfficiencyWarning(EfficiencyWarning):
     pass
 
+is_numpy1 = np.lib.NumpyVersion(np.__version__) < '2.0.0b1'
 
 cdef class Dense(base.Data):
     def __init__(self, data, shape=None, copy=True):
-        if np.lib.NumpyVersion(np.__version__) < '2.0.0b1':
+        if is_numpy1:
             # np2 accept None which act as np1's False
             copy = builtins.bool(copy)
         base = np.array(data, dtype=np.complex128, order='K', copy=copy)

qutip/core/dimensions.py

@@ -352,6 +352,34 @@ def _frozen(*args, **kwargs):
     raise RuntimeError("Dimension cannot be modified.")
 
 
+def einsum(subscripts, *operands):
+    """
+    Implementation of numpy.einsum for Qobj.
+    Evaluates the Einstein summation convention on the operands.
+    Parameters
+    ----------
+    subscripts: str
+        Specifies the subscripts for summation as comma
+        separated list of subscript labels.
+    operands: list of array_like
+        These are the arrays for the operation.
+
+    Returns
+    -------
+    Qobj (numpy.complex128)
+        Result of einsum as Qobj (numpy.complex128 if result is scalar)
+    """
+    operands_array = [to_tensor_rep(op) for op in operands]
+    result = np.einsum(subscripts, *operands_array)
+    if result.shape == ():
+        return result
+    dims = [
+        [d for d in result.shape[:result.ndim // 2]],
+        [d for d in result.shape[result.ndim // 2:]]
+    ]
+    return from_tensor_rep(result, dims)
+
+
 class MetaSpace(type):
     def __call__(cls, *args: SpaceLike, rep: str = None) -> "Space":
         """

qutip/tests/core/test_dimensions.py

@@ -6,7 +6,7 @@
 from qutip.core.dimensions import (
     flatten, unflatten, enumerate_flat, deep_remove, deep_map,
     dims_idxs_to_tensor_idxs, dims_to_tensor_shape, dims_to_tensor_perm,
-    collapse_dims_super, collapse_dims_oper, Dimensions
+    collapse_dims_super, collapse_dims_oper, einsum, Dimensions
 )
 
 
@@ -189,3 +189,17 @@ def test_dims_comparison():
     assert Dimensions([[1], [2]])[0] != Dimensions([[1], [2]])[1]
     assert not Dimensions([[1], [2]])[1] != Dimensions([[1], [2]])[1]
     assert not Dimensions([[1], [2]])[0] != Dimensions([[1], [2]])[0]
+
+
+@pytest.mark.parametrize(["subscripts", "operands", "expected"], [
+    pytest.param("ii", [qutip.sigmaz()], 0),
+    pytest.param("ij", [qutip.sigmax()], qutip.sigmax()),
+    pytest.param("ij->ji", [qutip.sigmay()], qutip.sigmay().trans()),
+    pytest.param("ij,ji", [qutip.sigmaz(), qutip.sigmaz()], 2),
+    pytest.param("ijij", [qutip.tensor(qutip.thermal_dm(2,1), qutip.thermal_dm(2,1))], 1),
+    pytest.param("ikjl,jm->ikml", [qutip.tensor(qutip.sigmaz(), qutip.sigmaz()), 
+                             qutip.sigmaz()], qutip.tensor(qutip.qeye(2), qutip.sigmaz())),
+    pytest.param("ijkl->kjil", [qutip.tensor(qutip.sigmam(), qutip.sigmaz())], qutip.tensor(qutip.sigmap(), qutip.sigmaz()))
+])
+def test_einsum(subscripts, operands, expected):
+    assert einsum(subscripts, *operands) == expected