Upgrade qml.structure_constants to handle dense matrices (#6861)

Integrate structure constants dense into [sc-81966] Todo - [x] update and unify documentation --------- Co-authored-by: David Wierichs <[email protected]> Co-authored-by: Yushao Chen (Jerry) <[email protected]> Co-authored-by: ANTH0NY <[email protected]> Co-authored-by: Diego <[email protected]>
PennyLaneAI · Mar 5, 2025 · 7bdf71b · 7bdf71b
1 parent 3db80cd
commit 7bdf71b
Show file tree

Hide file tree

Showing 8 changed files with 249 additions and 262 deletions.
diff --git a/doc/releases/changelog-dev.md b/doc/releases/changelog-dev.md
@@ -41,6 +41,9 @@
   Also added ``qml.pauli.trace_inner_product`` that can handle batches of dense matrices.
   [(#6811)](https://github.com/PennyLaneAI/pennylane/pull/6811)
 
+* ``qml.structure_constants`` now accepts and outputs matrix inputs using the ``matrix`` keyword.
+  [(#6861)](https://github.com/PennyLaneAI/pennylane/pull/6861)
+
 <h3>Improvements 🛠</h3>
 
 * Added a class `qml.capture.transforms.MergeAmplitudeEmbedding` that merges `qml.AmplitudeEmbedding` operators
@@ -326,6 +329,9 @@
 * ``pennylane.labs.dla.lie_closure_dense`` is removed and integrated into ``qml.lie_closure`` using the new ``dense`` keyword.
   [(#6811)](https://github.com/PennyLaneAI/pennylane/pull/6811)
 
+* ``pennylane.labs.dla.structure_constants_dense`` is removed and integrated into ``qml.structure_constants`` using the new ``matrix`` keyword.
+  [(#6861)](https://github.com/PennyLaneAI/pennylane/pull/6861)
+
 * ``ResourceOperator.resource_params`` is changed to a property.
   [(#6973)](https://github.com/PennyLaneAI/pennylane/pull/6973)
 

diff --git a/pennylane/labs/dla/__init__.py b/pennylane/labs/dla/__init__.py
@@ -20,7 +20,6 @@
 .. autosummary::
     :toctree: api
 
-    ~structure_constants_dense
     ~cartan_decomp
     ~recursive_cartan_decomp
     ~cartan_subalgebra
@@ -80,7 +79,6 @@
 
 """
 
-from .structure_constants_dense import structure_constants_dense
 from .cartan import (
     cartan_decomp,
     recursive_cartan_decomp,

diff --git a/pennylane/labs/dla/cartan_subalgebra.py b/pennylane/labs/dla/cartan_subalgebra.py
@@ -173,9 +173,8 @@ def cartan_subalgebra(
         all remaining operators from ``m``.
 
         >>> import numpy as np
-        >>> from pennylane.labs.dla import structure_constants_dense
         >>> g = np.vstack([k, m]) # re-order g to separate k and m operators
-        >>> adj = structure_constants_dense(g) # compute adjoint representation of g
+        >>> adj = qml.structure_constants(g, matrix=True) # compute adjoint representation of g
 
         Finally, we can compute a Cartan subalgebra :math:`\mathfrak{a}`, a maximal Abelian subalgebra of :math:`\mathfrak{m}`.
 

diff --git a/pennylane/labs/dla/structure_constants_dense.py b/pennylane/labs/dla/structure_constants_dense.py
diff --git a/pennylane/labs/tests/dla/test_structure_constants_dense.py b/pennylane/labs/tests/dla/test_structure_constants_dense.py
diff --git a/pennylane/labs/tests/dla/test_variational_kak.py b/pennylane/labs/tests/dla/test_variational_kak.py
@@ -25,7 +25,6 @@
     check_cartan_decomp,
     concurrence_involution,
     orthonormalize,
-    structure_constants_dense,
     validate_kak,
     variational_kak_adj,
 )
@@ -54,7 +53,7 @@ def test_kak_Ising(n, dense):
         adj = qml.structure_constants(g)
     else:
         g = np.vstack([k, m])
-        adj = structure_constants_dense(g)
+        adj = qml.structure_constants(g, matrix=True)
 
     g, k, mtilde, h, adj = cartan_subalgebra(g, k, m, adj, tol=1e-10, start_idx=0)
 
@@ -92,7 +91,7 @@ def test_kak_Heisenberg(n, dense):
         adj = qml.structure_constants(g)
     else:
         g = np.vstack([k, m])
-        adj = structure_constants_dense(g)
+        adj = qml.structure_constants(g, matrix=True)
 
     g, k, mtilde, h, adj = cartan_subalgebra(g, k, m, adj, tol=1e-10, start_idx=0)
 
@@ -132,7 +131,7 @@ def test_kak_Heisenberg_summed(is_orthogonal, dense):
         adj = qml.structure_constants(g, is_orthogonal=is_orthogonal)
     else:
         g = np.vstack([k, m])
-        adj = structure_constants_dense(g, is_orthonormal=is_orthogonal)
+        adj = qml.structure_constants(g, matrix=True, is_orthogonal=is_orthogonal)
 
     g, k, mtilde, h, adj = cartan_subalgebra(
         g, k, m, adj, tol=1e-10, start_idx=0, is_orthogonal=is_orthogonal