Implement the Tensor class

.pylintdict

@@ -242,6 +242,7 @@ intel
 intelvem
 interatomic
 internuclear
+interoperability
 interpretable
 ints
 io
@@ -354,6 +355,7 @@ natom
 nbasis
 nd
 ndarray
+ndim
 nelec
 neq
 networkx
@@ -583,6 +585,7 @@ uccsd
 ucrx
 ucry
 ucrz
+ufuncs
 uhf
 ulimit
 un

qiskit_nature/second_q/hamiltonians/lattices/hyper_cubic_lattice.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -30,7 +30,7 @@ class HyperCubicLattice(Lattice):
     tuples of `size`, `edge_parameters`, and `boundary_conditions`.
     For example,
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.hamiltonians.lattices import (
             BoundaryCondition,

qiskit_nature/second_q/operators/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2022.
+# (C) Copyright IBM 2022, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -28,6 +28,7 @@
    VibrationalOp
    VibrationalIntegrals
    PolynomialTensor
+   Tensor
 
 Modules
 -------
@@ -46,6 +47,7 @@
 from .vibrational_integrals import VibrationalIntegrals
 from .polynomial_tensor import PolynomialTensor
 from .sparse_label_op import SparseLabelOp
+from .tensor import Tensor
 
 __all__ = [
     "ElectronicIntegrals",
@@ -55,4 +57,5 @@
     "VibrationalIntegrals",
     "PolynomialTensor",
     "SparseLabelOp",
+    "Tensor",
 ]

qiskit_nature/second_q/operators/electronic_integrals.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2022.
+# (C) Copyright IBM 2022, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -25,7 +25,8 @@
 from qiskit_nature.exceptions import QiskitNatureError
 import qiskit_nature.optionals as _optionals
 
-from .polynomial_tensor import ARRAY_TYPE, PolynomialTensor
+from .polynomial_tensor import PolynomialTensor
+from .tensor import Tensor
 from .tensor_ordering import (
     IndexType,
     find_index_order,
@@ -256,8 +257,8 @@ def alpha_beta(self) -> PolynomialTensor:
         if self.beta_alpha.is_empty():
             return self.beta_alpha
 
-        beta_alpha = cast(ARRAY_TYPE, self.beta_alpha["++--"])
-        alpha_beta = np.moveaxis(beta_alpha, (0, 1), (2, 3))
+        # TODO: remove extra-wrapping of Tensor once settings.tensor_unwrapping is removed
+        alpha_beta = Tensor(np.moveaxis(Tensor(self.beta_alpha["++--"]), (0, 1), (2, 3)))
         return PolynomialTensor({"++--": alpha_beta}, validate=False)
 
     @property
@@ -378,7 +379,7 @@ def _add(self, other: ElectronicIntegrals, qargs=None) -> ElectronicIntegrals:
     @classmethod
     def apply(
         cls,
-        function: Callable[..., np.ndarray | SparseArray | Number],
+        function: Callable[..., np.ndarray | SparseArray | complex],
         *operands: ElectronicIntegrals,
         validate: bool = True,
     ) -> ElectronicIntegrals:
@@ -568,9 +569,7 @@ def second_q_coeffs(self) -> PolynomialTensor:
 
         kron_one_body = np.zeros((2, 2))
         kron_two_body = np.zeros((2, 2, 2, 2))
-        kron_tensor = PolynomialTensor(
-            {"": cast(Number, 1.0), "+-": kron_one_body, "++--": kron_two_body}
-        )
+        kron_tensor = PolynomialTensor({"": 1.0, "+-": kron_one_body, "++--": kron_two_body})
 
         if beta_empty and beta_alpha_empty:
             kron_one_body[(0, 0)] = 1

qiskit_nature/second_q/operators/fermionic_op.py

@@ -1,6 +1,6 @@
 # This code is part of Qiskit.
 #
-# (C) Copyright IBM 2021, 2022.
+# (C) Copyright IBM 2021, 2023.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -46,7 +46,7 @@ class FermionicOp(SparseLabelOp):
     A ``FermionicOp`` is initialized with a dictionary, mapping terms to their respective
     coefficients:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         from qiskit_nature.second_q.operators import FermionicOp
 
@@ -62,7 +62,7 @@ class FermionicOp(SparseLabelOp):
     If you have very restricted memory resources available, or would like to avoid the additional
     copy, the dictionary will be stored by reference if you disable ``copy`` like so:
 
-    .. jupyter-execute::
+    .. code-block:: python
 
         some_big_data = {
             "+_0 -_0": 1.0,
@@ -91,40 +91,40 @@ class FermionicOp(SparseLabelOp):
 
     Addition
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       FermionicOp({"+_1": 1}, num_spin_orbitals=2) + FermionicOp({"+_0": 1}, num_spin_orbitals=2)
 
     Sum
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       sum(FermionicOp({label: 1}, num_spin_orbitals=3) for label in ["+_0", "-_1", "+_2 -_2"])
 
     Scalar multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       0.5 * FermionicOp({"+_1": 1}, num_spin_orbitals=2)
 
     Operator multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op1 = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
       op2 = FermionicOp({"-_0 +_0 +_1": 1}, num_spin_orbitals=2)
       print(op1 @ op2)
 
     Tensor multiplication
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       op = FermionicOp({"+_0 -_1": 1}, num_spin_orbitals=2)
       print(op ^ op)
 
     Adjoint
 
-    .. jupyter-execute::
+    .. code-block:: python
 
       FermionicOp({"+_0 -_1": 1j}, num_spin_orbitals=2).adjoint()
 
@@ -258,6 +258,7 @@ def from_polynomial_tensor(cls, tensor: PolynomialTensor) -> FermionicOp:
                 data[""] = cast(float, tensor[key])
                 continue
 
+            # TODO: extract label_template into Tensor class
             label_template = " ".join(f"{op}_{{}}" for op in key)
 
             # PERF: the following matrix unpacking is a performance bottleneck!