diff --git a/Makefile b/Makefile
index edfc64ec39..c99e23e3b9 100644
--- a/Makefile
+++ b/Makefile
@@ -78,7 +78,7 @@ environment:
 			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Catalyst --pre --upgrade;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Lightning --pre --upgrade;\
-			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane;\
+			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git@v0.41.0-rc0#egg=pennylane;\
 			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/XanaduAI/iqpopt.git#egg=iqpopt;\
 		fi;\
 	fi
diff --git a/_static/demonstration_assets/qnn_module/sphx_glr_qnn_module_tf_001.png b/_static/demonstration_assets/qnn_module/sphx_glr_qnn_module_tf_001.png
new file mode 100644
index 0000000000..1e7921e25e
Binary files /dev/null and b/_static/demonstration_assets/qnn_module/sphx_glr_qnn_module_tf_001.png differ
diff --git a/demonstrations/tutorial_qnn_module_tf.metadata.json b/demonstrations/qnn_module_tf.metadata.json
similarity index 93%
rename from demonstrations/tutorial_qnn_module_tf.metadata.json
rename to demonstrations/qnn_module_tf.metadata.json
index 36a7aab856..15d489d26a 100644
--- a/demonstrations/tutorial_qnn_module_tf.metadata.json
+++ b/demonstrations/qnn_module_tf.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2020-11-02T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-21T00:00:00+00:00",
     "categories": [
         "Devices and Performance",
         "Quantum Machine Learning"
diff --git a/demonstrations/tutorial_qnn_module_tf.py b/demonstrations/qnn_module_tf.py
similarity index 81%
rename from demonstrations/tutorial_qnn_module_tf.py
rename to demonstrations/qnn_module_tf.py
index 8f8c906c1c..d6cc1e5a91 100644
--- a/demonstrations/tutorial_qnn_module_tf.py
+++ b/demonstrations/qnn_module_tf.py
@@ -1,4 +1,7 @@
-"""
+r"""
+.. role:: html(raw)
+    :format: html
+
 Turning quantum nodes into Keras Layers
 =======================================
 
@@ -10,7 +13,13 @@
 
    tutorial_qnn_module_torch Turning quantum nodes into Torch Layers
 
-*Author: Tom Bromley — Posted: 02 November 2020. Last updated: 28 January 2021.*
+*Author: Tom Bromley — Posted: 02 November 2020. Last updated: 21 March 2025.*
+
+.. warning::
+
+    This demo is only compatible with PennyLane version 0.40 or below.
+    For usage with a later version of PennyLane, please consider using
+    :doc:`PyTorch </demos/tutorial_qnn_module_torch>` or :doc:`JAX </demos/tutorial_How_to_optimize_QML_model_using_JAX_and_Optax>`.
 
 Creating neural networks in `Keras <https://keras.io/>`__ is easy. Models are constructed from
 elementary *layers* and can be trained using a high-level API. For example, the following code
@@ -67,6 +76,11 @@
 plt.scatter(X[:, 0], X[:, 1], c=c)
 plt.show()
 
+##############################################################################
+# .. figure:: /_static/demonstration_assets/qnn_module/sphx_glr_qnn_module_tf_001.png
+#    :width: 100%
+#    :align: center
+
 ###############################################################################
 # Defining a QNode
 # ----------------
@@ -166,7 +180,25 @@ def qnode(inputs, weights):
 
 fitting = model.fit(X, y_hot, epochs=6, batch_size=5, validation_split=0.25, verbose=2)
 
-###############################################################################
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#  .. code-block:: none
+#
+#    Epoch 1/6
+#    30/30 - 4s - loss: 0.4153 - accuracy: 0.7333 - val_loss: 0.3183 - val_accuracy: 0.7800 - 4s/epoch - 123ms/step
+#    Epoch 2/6
+#    30/30 - 4s - loss: 0.2927 - accuracy: 0.8000 - val_loss: 0.2475 - val_accuracy: 0.8400 - 4s/epoch - 130ms/step
+#    Epoch 3/6
+#    30/30 - 4s - loss: 0.2272 - accuracy: 0.8333 - val_loss: 0.2111 - val_accuracy: 0.8200 - 4s/epoch - 119ms/step
+#    Epoch 4/6
+#    30/30 - 4s - loss: 0.1963 - accuracy: 0.8667 - val_loss: 0.1917 - val_accuracy: 0.8600 - 4s/epoch - 118ms/step
+#    Epoch 5/6
+#    30/30 - 4s - loss: 0.1772 - accuracy: 0.8667 - val_loss: 0.1828 - val_accuracy: 0.8600 - 4s/epoch - 117ms/step
+#    Epoch 6/6
+#    30/30 - 4s - loss: 0.1603 - accuracy: 0.8733 - val_loss: 0.2006 - val_accuracy: 0.8200 - 4s/epoch - 117ms/step
+#
+#
 # How did we do? The model looks to have successfully trained and the accuracy on both the
 # training and validation datasets is reasonably high. In practice, we would aim to push the
 # accuracy higher by thinking carefully about the model design and the choice of hyperparameters
@@ -224,7 +256,25 @@ def qnode(inputs, weights):
 
 fitting = model.fit(X, y_hot, epochs=6, batch_size=5, validation_split=0.25, verbose=2)
 
-###############################################################################
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#  .. code-block:: none
+#
+#    Epoch 1/6
+#    30/30 - 7s - loss: 0.3682 - accuracy: 0.7467 - val_loss: 0.2550 - val_accuracy: 0.8000 - 7s/epoch - 229ms/step
+#    Epoch 2/6
+#    30/30 - 7s - loss: 0.2428 - accuracy: 0.8067 - val_loss: 0.2105 - val_accuracy: 0.8400 - 7s/epoch - 224ms/step
+#    Epoch 3/6
+#    30/30 - 7s - loss: 0.2001 - accuracy: 0.8333 - val_loss: 0.1901 - val_accuracy: 0.8200 - 7s/epoch - 220ms/step
+#    Epoch 4/6
+#    30/30 - 7s - loss: 0.1816 - accuracy: 0.8600 - val_loss: 0.1776 - val_accuracy: 0.8200 - 7s/epoch - 224ms/step
+#    Epoch 5/6
+#    30/30 - 7s - loss: 0.1661 - accuracy: 0.8667 - val_loss: 0.1711 - val_accuracy: 0.8600 - 7s/epoch - 224ms/step
+#    Epoch 6/6
+#    30/30 - 7s - loss: 0.1520 - accuracy: 0.8600 - val_loss: 0.1807 - val_accuracy: 0.8200 - 7s/epoch - 221ms/step
+#
+#
 # Great! We've mastered the basics of constructing hybrid classical-quantum models using
 # PennyLane and Keras. Can you think of any interesting hybrid models to construct? How do they
 # perform on realistic datasets?
diff --git a/demonstrations/tutorial_classically_boosted_vqe.metadata.json b/demonstrations/tutorial_classically_boosted_vqe.metadata.json
index 797d86cf63..df8e6990f6 100644
--- a/demonstrations/tutorial_classically_boosted_vqe.metadata.json
+++ b/demonstrations/tutorial_classically_boosted_vqe.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2022-10-31T00:00:00+00:00",
-    "dateOfLastModification": "2024-12-13T00:00:00+00:00",
+    "dateOfLastModification": "2025-01-23T00:00:00+00:00",
     "categories": [
         "Quantum Chemistry"
     ],
diff --git a/demonstrations/tutorial_classically_boosted_vqe.py b/demonstrations/tutorial_classically_boosted_vqe.py
index dd65f9a332..986ec0a7b2 100644
--- a/demonstrations/tutorial_classically_boosted_vqe.py
+++ b/demonstrations/tutorial_classically_boosted_vqe.py
@@ -449,7 +449,7 @@ def hadamard_test(Uq, Ucl, component="real"):
 
     qml.Hadamard(wires=[0])
     qml.ControlledQubitUnitary(
-        Uq.conjugate().T @ Ucl, control_wires=[0], wires=wires[1:]
+        Uq.conjugate().T @ Ucl, wires=wires
     )
     qml.Hadamard(wires=[0])
 
diff --git a/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.metadata.json b/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.metadata.json
index 1efc93770b..a78b88eea4 100644
--- a/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.metadata.json
+++ b/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-12-19T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-10T00:00:00+00:00",
+    "dateOfLastModification": "2025-04-11T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Algorithms"
diff --git a/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.py b/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.py
index a4f5439a83..cb7ff2379b 100644
--- a/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.py
+++ b/demonstrations/tutorial_fixed_depth_hamiltonian_simulation_via_cartan_decomposition.py
@@ -20,7 +20,7 @@
 Introduction
 ------------
 
-The :doc:`KAK theorem </demos/tutorial_kak_decomposition>` is an important result from Lie theory that states that any Lie group element :math:`U` can be decomposed
+The :doc:`KAK decomposition </demos/tutorial_kak_decomposition>` is an important result from Lie theory that states that any Lie group element :math:`U` can be decomposed
 as :math:`U = K_1 A K_2,` where :math:`K_{1, 2}` and :math:`A` are elements of two special sub-groups
 :math:`\mathcal{K}` and :math:`\mathcal{A},` respectively. In special cases, the decomposition simplifies to :math:`U = K A K^\dagger.`
 
@@ -36,14 +36,14 @@
 We can use this general result from Lie theory as a powerful circuit decomposition technique.
 
 .. note:: We recommend a basic understanding of Lie algebras, see e.g. our :doc:`introduction to (dynamical) Lie algebras for quantum practitioners </demos/tutorial_liealgebra>`.
-    Otherwise, this demo should be self-contained, though for the mathematically inclined, we further recommend our :doc:`demo on the KAK theorem </demos/tutorial_kak_decomposition>`
-    that dives into the mathematical depths of the theorem and provides more background info.
+    Otherwise, this demo should be self-contained, though for the mathematically inclined, we further recommend our :doc:`demo on the KAK decomposition </demos/tutorial_kak_decomposition>`
+    that dives into the mathematical depths of the decomposition and provides more background info.
 
 Goal: Fast-forwarding time evolutions using the KAK decomposition
 -----------------------------------------------------------------
 
 Unitary gates in quantum computing are described by the special unitary Lie group :math:`SU(2^n),` so we can use the KAK
-theorem to decompose quantum gates into :math:`U = K_1 A K_2.` While the mathematical statement is rather straightforward,
+decomposition to factorize quantum gates into :math:`U = K_1 A K_2.` While the mathematical statement is rather straightforward,
 actually finding this decomposition is not. We are going to follow the recipe prescribed in 
 `Fixed Depth Hamiltonian Simulation via Cartan Decomposition <https://arxiv.org/abs/2104.00728>`__ [#Kökcü]_, 
 which tackles this decomposition on the level of the associated Lie algebra via Cartan decomposition.
@@ -74,6 +74,7 @@
 import numpy as np
 import pennylane as qml
 from pennylane import X, Y, Z
+from pennylane.liealg import even_odd_involution, cartan_decomp, horizontal_cartan_subalgebra
 
 import jax
 import jax.numpy as jnp
@@ -111,10 +112,8 @@
 # One common choice of involution is the so-called even-odd involution for Pauli words,
 # :math:`P = P_1 \otimes P_2 .. \otimes P_n,` where :math:`P_j \in \{I, X, Y, Z\}.`
 # It essentially counts whether the number of non-identity Pauli operators in the Pauli word is even or odd.
-
-def even_odd_involution(op):
-    [pw] = op.pauli_rep
-    return len(pw) % 2
+# It is readily available in PennyLane as :func:`~.pennylane.liealg.even_odd_involution`, which 
+# we already imported above.
 
 even_odd_involution(X(0)), even_odd_involution(X(0) @ Y(3))
 
@@ -126,30 +125,9 @@ def even_odd_involution(op):
 # sort the operators by whether or not they yield a plus or minus sign from the involution function.
 # This is possible because the operators and involution nicely align with the eigenspace decomposition.
 
-def cartan_decomposition(g, involution):
-    """Cartan Decomposition g = k + m
-    
-    Args:
-        g (List[PauliSentence]): the (dynamical) Lie algebra to decompose
-        involution (callable): Involution function :math:`\Theta(\cdot)` to act on PauliSentence ops, should return ``0/1`` or ``True/False``.
-    
-    Returns:
-        k (List[PauliSentence]): the vertical subspace :math:`\Theta(x) = x`
-        m (List[PauliSentence]): the horizontal subspace :math:`\Theta(x) = -x` """
-    m = []
-    k = []
-
-    for op in g:
-        if involution(op): # vertical space when involution returns True
-            k.append(op)
-        else: # horizontal space when involution returns False
-            m.append(op)
-    return k, m
-
-k, m = cartan_decomposition(g, even_odd_involution)
+k, m = cartan_decomp(g, even_odd_involution)
 len(g), len(k), len(m)
 
-
 ##############################################################################
 # We have successfully decomposed the 60-dimensional Lie algebra
 # into a 24-dimensional vertical subspace and a 36-dimensional subspace.
@@ -187,51 +165,11 @@ def cartan_decomposition(g, involution):
 # that commute with it.
 #
 # We then obtain a further split of the vector space :math:`\mathfrak{m} = \tilde{\mathfrak{m}} \oplus \mathfrak{h},`
-# where :math:`\tilde{\mathfrak{m}}` is just the remainder of :math:`\mathfrak{m}.`
+# where :math:`\tilde{\mathfrak{m}}` is just the remainder of :math:`\mathfrak{m}.` The function
+# :func:`~.pennylane.liealg.horizontal_cartan_subalgebra` returns some additional information, which we will
+# not use here.
 
-def _commutes_with_all(candidate, ops):
-    r"""Check if ``candidate`` commutes with all ``ops``"""
-    for op in ops:
-        com = candidate.commutator(op)
-        com.simplify()
-        
-        if not len(com) == 0:
-            return False
-    return True
-
-def cartan_subalgebra(m, which=0):
-    """Compute the Cartan subalgebra from the horizontal subspace :math:`\mathfrak{m}`
-    of the Cartan decomposition
-
-    This implementation is specific for cases of bases of m with pure Pauli words as
-    detailed in Appendix C in `2104.00728 <https://arxiv.org/abs/2104.00728>`__.
-    
-    Args:
-        m (List[PauliSentence]): the horizontal subspace :math:`\Theta(x) = -x
-        which (int): Choice for the initial element of m from which to construct 
-            the maximal Abelian subalgebra
-    
-    Returns:
-        mtilde (List): remaining elements of :math:`\mathfrak{m}`
-            s.t. :math:`\mathfrak{m} = \tilde{\mathfrak{m}} \oplus \mathfrak{h}`.
-        h (List): Cartan subalgebra :math:`\mathfrak{h}`.
-
-    """
-
-    h = [m[which]] # first candidate
-    mtilde = m.copy()
-
-    for m_i in m:
-        if _commutes_with_all(m_i, h):
-            if m_i not in h:
-                h.append(m_i)
-    
-    for h_i in h:
-        mtilde.remove(h_i)
-    
-    return mtilde, h
-
-mtilde, h = cartan_subalgebra(m)
+g, k, mtilde, h, _ = horizontal_cartan_subalgebra(k, m, tol=1e-8)
 len(g), len(k), len(mtilde), len(h)
 
 ##############################################################################
@@ -241,7 +179,7 @@ def cartan_subalgebra(m, which=0):
 # Variational KhK decomposition
 # -----------------------------
 #
-# The KAK theorem is not constructive in the sense that it proves that there exists such a decomposition, but there is no general way of obtaining
+# The KAK decomposition is not constructive in the sense that it proves that there exists such a decomposition, but there is no general way of obtaining
 # it. In particular, there are no linear algebra subroutines implemented in ``numpy`` or ``scipy`` that just compute it for us.
 # Here, we follow the construction of [#Kökcü]_ for the special case of :math:`H` being in the horizontal space and the decomposition
 # simplifying to :math:`H = K^\dagger h K`.
@@ -282,10 +220,10 @@ def cartan_subalgebra(m, which=0):
 #
 
 def run_opt(
-    value_and_grad,
+    loss,
     theta,
-    n_epochs=500,
-    lr=0.1,
+    n_epochs=1000,
+    lr=0.05,
 ):
     """Boilerplate JAX optimization"""
     value_and_grad = jax.jit(jax.value_and_grad(loss))
@@ -336,7 +274,7 @@ def loss(theta):
     A = K_m @ v_m @ K_m.conj().T
     return jnp.trace(A.conj().T @ H_m).real
 
-theta0 = jnp.ones(len(k), dtype=float)
+theta0 = jax.random.normal(jax.random.PRNGKey(0), shape=(len(k),), dtype=float)
 
 thetas, energy, _ = run_opt(loss, theta0, n_epochs=1000, lr=0.05)
 plt.plot(energy - np.min(energy))
@@ -359,13 +297,18 @@ def loss(theta):
 # .. math:: h_0 = K_c^\dagger H K_c.
 
 h_0_m = Kc_m.conj().T @ H_m @ Kc_m
-h_0 = qml.pauli_decompose(h_0_m)
 
-print(len(h_0))
+# decompose h_0_m in terms of the basis of h
+basis = [qml.matrix(op, wire_order=range(n_wires)) for op in h]
+coeffs = qml.pauli.trace_inner_product(h_0_m, basis)
+
+# ensure that decomposition is correct, i.e. h_0_m is truely an element of just h
+h_0_m_recomposed = np.sum([c * op for c, op in zip(coeffs, basis)], axis=0)
+print("Decomposition of h_0 is faithful: ", np.allclose(h_0_m_recomposed, h_0_m, atol=1e-10))
+
+# sanity check that the horizontal CSA is Abelian, i.e. all its elements commute
+print("All elements in h commute with each other: ", qml.liealg.check_abelian(h))
 
-# assure that h_0 is in \mathfrak{h}
-h_vspace = qml.pauli.PauliVSpace(h)
-not h_vspace.is_independent(h_0.pauli_rep)
 
 ##############################################################################
 #
@@ -393,6 +336,7 @@ def loss(theta):
 t = 1.
 U_exact = qml.exp(-1j * t * H)
 U_exact_m = qml.matrix(U_exact, wire_order=range(n_wires))
+h_0  = qml.dot(coeffs, h)
 
 def U_kak(theta_opt, t):
     qml.adjoint(K)(theta_opt, k)
@@ -478,7 +422,7 @@ def compute_res(Us):
 # Conclusion
 # ----------
 #
-# The KAK theorem is a very general mathematical result with far-reaching consequences.
+# The KAK decomposition is a very general mathematical result with far-reaching consequences.
 # While there is no canonical way of obtaining an actual decomposition in practice, we followed
 # the approach of [#Kökcü]_ which uses a specifically designed loss function and variational
 # optimization to find the decomposition.
diff --git a/demonstrations/tutorial_how_to_import_qiskit_noise_models.metadata.json b/demonstrations/tutorial_how_to_import_qiskit_noise_models.metadata.json
index 78b1bd040a..3786483a90 100644
--- a/demonstrations/tutorial_how_to_import_qiskit_noise_models.metadata.json
+++ b/demonstrations/tutorial_how_to_import_qiskit_noise_models.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-11-25T00:00:00+00:00",
-    "dateOfLastModification": "2024-11-25T00:00:00+00:00",
+    "dateOfLastModification": "2025-04-11T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "How-to"
diff --git a/demonstrations/tutorial_how_to_import_qiskit_noise_models.py b/demonstrations/tutorial_how_to_import_qiskit_noise_models.py
index 7568184d0e..887921307f 100644
--- a/demonstrations/tutorial_how_to_import_qiskit_noise_models.py
+++ b/demonstrations/tutorial_how_to_import_qiskit_noise_models.py
@@ -15,6 +15,7 @@
 custom user-defined or fake backend-based noise models.
 """
 
+
 ######################################################################
 # Noise models in Qiskit and PennyLane
 # ------------------------------------
diff --git a/demonstrations/tutorial_kak_decomposition.metadata.json b/demonstrations/tutorial_kak_decomposition.metadata.json
index 94e43e6012..0c1bec711c 100644
--- a/demonstrations/tutorial_kak_decomposition.metadata.json
+++ b/demonstrations/tutorial_kak_decomposition.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-11-25T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-07T09:00:00+00:00",
+    "dateOfLastModification": "2025-03-26T09:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Algorithms"
diff --git a/demonstrations/tutorial_kak_decomposition.py b/demonstrations/tutorial_kak_decomposition.py
index a4789f8a4e..2d341a1b41 100644
--- a/demonstrations/tutorial_kak_decomposition.py
+++ b/demonstrations/tutorial_kak_decomposition.py
@@ -348,14 +348,15 @@ def is_orthogonal(op, basis):
 #
 # Let us define it in code, and check whether it gives rise to a Cartan decomposition.
 # As we want to look at another example later, we wrap everything in a function.
+# A similar function is available in PennyLane as :func:`~.pennylane.liealg.check_cartan_decomp`.
 #
 
 
-def check_cartan_decomposition(g, k, space_name):
+def check_cartan_decomp(g, k, space_name):
     """Given an algebra g and an operator subspace k, verify that k is a subalgebra
-    and gives rise to a Cartan decomposition."""
+    and gives rise to a Cartan decomposition. Similar to qml.liealg.check_cartan_decomp"""
     # Check Lie closure of k
-    k_lie_closure = qml.pauli.dla.lie_closure(k)
+    k_lie_closure = qml.lie_closure(k)
     k_is_closed = len(k_lie_closure) == len(k)
     print(f"The Lie closure of k is as big as k itself: {k_is_closed}.")
 
@@ -387,7 +388,7 @@ def check_cartan_decomposition(g, k, space_name):
 
 u1 = [Z(0)]
 space_name = "SU(2)/U(1)"
-p = check_cartan_decomposition(su2, u1, space_name)
+p = check_cartan_decomp(su2, u1, space_name)
 
 ######################################################################
 # Cartan subalgebras
@@ -811,7 +812,7 @@ def theta_Y(x):
 # Define subalgebra su(2) ⊕ su(2)
 su2_su2 = [X(0), Y(0), Z(0), X(1), Y(1), Z(1)]
 space_name = "SU(4)/(SU(2)xSU(2))"
-p = check_cartan_decomposition(su4, su2_su2, space_name)
+p = check_cartan_decomp(su4, su2_su2, space_name)
 
 ######################################################################
 # .. admonition:: Math detail: involution for two-qubit decomposition
diff --git a/demonstrations/tutorial_liesim.metadata.json b/demonstrations/tutorial_liesim.metadata.json
index f3d75d959d..a307de56e6 100644
--- a/demonstrations/tutorial_liesim.metadata.json
+++ b/demonstrations/tutorial_liesim.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-06-07T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-17T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Getting Started"
diff --git a/demonstrations/tutorial_liesim.py b/demonstrations/tutorial_liesim.py
index 14f91b9a05..20630ee3da 100644
--- a/demonstrations/tutorial_liesim.py
+++ b/demonstrations/tutorial_liesim.py
@@ -186,7 +186,7 @@
 # work with PauliSentence instances for efficiency
 generators = [op.pauli_rep for op in generators]
 
-dla = qml.pauli.lie_closure(generators, pauli=True)
+dla = qml.lie_closure(generators, pauli=True)
 dim_g = len(dla)
 
 ##############################################################################
@@ -239,7 +239,7 @@
 # the forward pass of the expectation value computation. For demonstration purposes,
 # we choose a random subset of ``depth=10`` generators for gates from the DLA.
 
-adjoint_repr = qml.pauli.structure_constants(dla)
+adjoint_repr = qml.structure_constants(dla)
 
 depth = 10
 gate_choice = np.random.choice(dim_g, size=depth)
diff --git a/demonstrations/tutorial_liesim_extension.metadata.json b/demonstrations/tutorial_liesim_extension.metadata.json
index 7da2c15c82..1807c7b04a 100644
--- a/demonstrations/tutorial_liesim_extension.metadata.json
+++ b/demonstrations/tutorial_liesim_extension.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-06-18T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-13T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-26T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Quantum Machine Learning"
diff --git a/demonstrations/tutorial_liesim_extension.py b/demonstrations/tutorial_liesim_extension.py
index b11a640b6e..220702874e 100644
--- a/demonstrations/tutorial_liesim_extension.py
+++ b/demonstrations/tutorial_liesim_extension.py
@@ -99,9 +99,9 @@ def TFIM(n):
     generators += [Z(i) for i in range(n)]
     generators = [op.pauli_rep for op in generators]
 
-    dla = qml.pauli.lie_closure(generators, pauli=True)
-    dim_dla = len(dla)
-    return generators, dla, dim_dla
+    dla = qml.lie_closure(generators, pauli=True)
+    dim_g = len(dla)
+    return generators, dla, dim_g
 
 generators, dla, dim_g = TFIM(n=4)
 
diff --git a/demonstrations/tutorial_noisy_circuits.metadata.json b/demonstrations/tutorial_noisy_circuits.metadata.json
index 92522adae3..0ba26ef0b7 100644
--- a/demonstrations/tutorial_noisy_circuits.metadata.json
+++ b/demonstrations/tutorial_noisy_circuits.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2021-02-22T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2024-12-18T00:00:00+00:00",
     "categories": [
         "Getting Started"
     ],
diff --git a/demonstrations/tutorial_noisy_circuits.py b/demonstrations/tutorial_noisy_circuits.py
index 1491215009..af1a7f4d7e 100644
--- a/demonstrations/tutorial_noisy_circuits.py
+++ b/demonstrations/tutorial_noisy_circuits.py
@@ -144,12 +144,13 @@ def bitflip_circuit(p):
     qml.CNOT(wires=[0, 1])
     qml.BitFlip(p, wires=0)
     qml.BitFlip(p, wires=1)
-    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
+    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)), qml.state()
 
 
 ps = [0.001, 0.01, 0.1, 0.2]
 for p in ps:
-    print(f"QNode output for bit flip probability {p} is {bitflip_circuit(p):.4f}")
+    result = bitflip_circuit(p)
+    print(f"QNode output for bit flip probability {p} is {result[0]:.4f}")
 
 
 ######################################################################
@@ -158,7 +159,7 @@ def bitflip_circuit(p):
 # mitigation and error correction are so important. We can use PennyLane to look under the hood and
 # see the output state of the circuit for the largest noise parameter
 
-print(f"Output state for bit flip probability {p} is \n{np.real(dev.state)}")
+print(f"Output state for bit flip probability {p} is \n{result[1]}")
 
 ######################################################################
 # Besides the bit flip channel, PennyLane supports several other noisy channels that are commonly
diff --git a/demonstrations/tutorial_odegen.metadata.json b/demonstrations/tutorial_odegen.metadata.json
index ab2eb2a794..df0ad04903 100644
--- a/demonstrations/tutorial_odegen.metadata.json
+++ b/demonstrations/tutorial_odegen.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-12-12T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-01-28T00:00:00+00:00",
     "categories": [
         "Optimization",
         "Quantum Computing",
diff --git a/demonstrations/tutorial_odegen.py b/demonstrations/tutorial_odegen.py
index 257bb3884c..8894ff68d8 100644
--- a/demonstrations/tutorial_odegen.py
+++ b/demonstrations/tutorial_odegen.py
@@ -45,10 +45,10 @@
 that can be executed on hardware.
 
 
-SPS & ODEgen
-------------
+SPS and ODEgen gradient rules
+-----------------------------
 
-Let us start by deriving both the SPS rule and ODEgen.
+Let us start by deriving both the SPS and ODEgen rules.
 
 We are interested in cost functions of the form
 
@@ -163,6 +163,7 @@
 Let us define it in PennyLane and also import some libraries that we are going to need for this demo.
 
 """
+
 import pennylane as qml
 import numpy as np
 import jax.numpy as jnp
@@ -181,21 +182,21 @@
 
 ##############################################################################
 # We are going to consider a system of transmon qubits described by the Hamiltonian
-# 
+#
 # .. math:: H(\theta, t) = - \sum_i \frac{\omega_i}{2} Z_i + \sum_i \Omega_i(t) \sin(\nu_i t + \phi_i(t)) Y_i + \sum_{q, p \in \mathcal{C}} \frac{g_{qp}}{2} (X_i X_p + Y_i Y_p).
-# 
-# The first term describes the single qubits with frequencies :math:`\omega_i.` 
-# The second term describes the driving with drive amplitudes :math:`\Omega_i,` drive frequencies :math:`\nu_i` and phases :math:`\phi_i.` 
-# You can check out our :doc:`recent demo on driving qubits on OQC's Lucy </demos/oqc_pulse>` if 
+#
+# The first term describes the single qubits with frequencies :math:`\omega_i.`
+# The second term describes the driving with drive amplitudes :math:`\Omega_i,` drive frequencies :math:`\nu_i` and phases :math:`\phi_i.`
+# You can check out our :doc:`recent demo on driving qubits on OQC's Lucy </demos/oqc_pulse>` if
 # you want to learn more about the details of controlling transmon qubits.
-# The third term describes the coupling between neighboring qubits. We only have two qubits and a simple topology of 
+# The third term describes the coupling between neighboring qubits. We only have two qubits and a simple topology of
 # :math:`\mathcal{C} = \{(0, 1)\},` hence only one coupling constant :math:`g_{01}.`
-# The coupling is necessary to generate entanglement, which is achieved with cross-resonant driving in fixed-coupling 
+# The coupling is necessary to generate entanglement, which is achieved with cross-resonant driving in fixed-coupling
 # transmon systems, as is the case here.
-# 
-# We will use realistic parameters for the transmons, taken from the `coaxmon design paper <https://arxiv.org/abs/1905.05670>`_ [#Patterson]_ 
+#
+# We will use realistic parameters for the transmons, taken from the `coaxmon design paper <https://arxiv.org/abs/1905.05670>`_ [#Patterson]_
 # (this is the blue-print for the transmon qubits in OQC's Lucy that you can :doc:`access on a pulse level in PennyLane </demos/oqc_pulse>`).
-# In order to prepare the singlet ground state, we will perform a cross-resonance pulse, i.e. driving one qubit at its coupled neighbor's 
+# In order to prepare the singlet ground state, we will perform a cross-resonance pulse, i.e. driving one qubit at its coupled neighbor's
 # frequency for entanglement generation (see [#Patterson]_ or [#Krantz]_) while simultaneously driving the other qubit on resonance.
 # We choose a gate time of :math:`100 \text{ ns}.` We will use a piecewise constant function :func:`~pennylane.pulse.pwc` to parametrize both
 # the amplitude :math:`\Omega_i(t)` and the phase :math:`\phi_i(t)` in time, with ``t_bins = 10`` time bins to allow for enough flexibility in the evolution.
@@ -221,7 +222,7 @@ def wrapped(p, t):
 H_pulse += drive_field(T_CR, qubit_freq[0]) * qml.PauliY(wires[1]) # off-resonance driving of qubit 1
 
 ##############################################################################
-# We can now define the cost function that computes the expectation value of 
+# We can now define the cost function that computes the expectation value of
 # the Heisenberg Hamiltonian after evolving the state with the parametrized pulse Hamiltonian.
 # We then define the two separate qnodes with ODEgen and SPS as their differentiation methods, respectively.
 
@@ -235,7 +236,14 @@ def qnode0(params):
 value_and_grad_jax = jax.jit(jax.value_and_grad(qnode_jax))
 
 num_split_times = 8
-qnode_sps = qml.QNode(qnode0, dev, interface="jax", diff_method=qml.gradients.stoch_pulse_grad, use_broadcasting=True, num_split_times=num_split_times)
+gradient_kwargs = {"use_broadcasting": True, "num_split_times": num_split_times}
+qnode_sps = qml.QNode(
+    qnode0,
+    dev,
+    interface="jax",
+    diff_method=qml.gradients.stoch_pulse_grad,
+    gradient_kwargs=gradient_kwargs,
+)
 value_and_grad_sps = jax.value_and_grad(qnode_sps)
 
 qnode_odegen = qml.QNode(qnode0, dev, interface="jax", diff_method=qml.gradients.pulse_odegen)
@@ -304,12 +312,12 @@ def partial_step(grad_circuit, opt_state, theta):
 
 
 ##############################################################################
-# We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path 
+# We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path
 # towards the minimum due to the stochasticity of the gradient estimates. Note that both optimizations start from the same (random) initial point.
 # This picture solidifies when repeating this procedure for multiple runs from different random initializations, as was demonstrated in [#Kottmann]_.
 #
 # We also want to make sure that this is a fair comparison in terms of quantum resources. In the case of ODEgen, we maximally have :math:`\mathcal{R}_\text{ODEgen} = 2 (4^n - 1) = 30` expectation values.
-# For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer 
+# For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer
 # quantum resources for ODEgen gradients while achieving better performance.
 #
 # Conclusion
@@ -323,9 +331,8 @@ def partial_step(grad_circuit, opt_state, theta):
 # Running VQE using ODEgen on hardware has recently been demonstrated in [#Kottmann]_ and you can directly find `the code here <https://github.com/XanaduAI/Analytic_Pulse_Gradients/tree/main/VQE_OQC>`_.
 
 
-
 ##############################################################################
-# 
+#
 # References
 # ----------
 #
diff --git a/demonstrations/tutorial_pulse_programming101.metadata.json b/demonstrations/tutorial_pulse_programming101.metadata.json
index 7801031d1f..9c5a8f71c3 100644
--- a/demonstrations/tutorial_pulse_programming101.metadata.json
+++ b/demonstrations/tutorial_pulse_programming101.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-03-08T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-02-14T00:00:00+00:00",
     "categories": [
         "Quantum Hardware",
         "Quantum Computing"
diff --git a/demonstrations/tutorial_pulse_programming101.py b/demonstrations/tutorial_pulse_programming101.py
index b1b4ce33ea..aa050030a1 100644
--- a/demonstrations/tutorial_pulse_programming101.py
+++ b/demonstrations/tutorial_pulse_programming101.py
@@ -322,15 +322,21 @@ def wrapped(p, t):
 
 ##############################################################################
 # Now we define the ``qnode`` that computes the expectation value of the molecular Hamiltonian.
+# We need to wrap the ``qnode`` in a function so that we can convert the expectation value to a real number.
+# This will enable use to make use of gradient descent methods that require real-valued loss functions.
 
 dev = qml.device("default.qubit", wires=range(n_wires))
 
-@qml.qnode(dev, interface="jax")
 def qnode(theta, t=duration):
-    qml.BasisState(jnp.array(data.tapered_hf_state), wires=H_obj.wires)
-    qml.evolve(H_pulse)(params=(*theta, *theta), t=t)
-    return qml.expval(H_obj)
 
+    @qml.qnode(dev)
+    def _qnode_inner(theta, t=duration):
+        qml.BasisState(jnp.array(data.tapered_hf_state), wires=H_obj.wires)
+        qml.evolve(H_pulse)(params=(*theta, *theta), t=t)
+        return qml.expval(H_obj)
+
+    expectation_value = _qnode_inner(theta, t)  # Execute the qnode
+    return jnp.real(expectation_value)  # Typecast to real number
 
 value_and_grad = jax.jit(jax.value_and_grad(qnode))
 
diff --git a/demonstrations/tutorial_qnn_module_torch.metadata.json b/demonstrations/tutorial_qnn_module_torch.metadata.json
index bfd569d229..f7e3189dc6 100644
--- a/demonstrations/tutorial_qnn_module_torch.metadata.json
+++ b/demonstrations/tutorial_qnn_module_torch.metadata.json
@@ -26,7 +26,7 @@
     "relatedContent": [
         {
             "type": "demonstration",
-            "id": "tutorial_qnn_module_tf",
+            "id": "qnn_module_tf",
             "weight": 1.0
         }
     ]
diff --git a/demonstrations/tutorial_qnn_module_torch.py b/demonstrations/tutorial_qnn_module_torch.py
index f3f59a77ff..253d749eca 100644
--- a/demonstrations/tutorial_qnn_module_torch.py
+++ b/demonstrations/tutorial_qnn_module_torch.py
@@ -8,7 +8,7 @@
 
 .. related::
 
-   tutorial_qnn_module_tf Turning quantum nodes into Keras Layers
+   qnn_module_tf Turning quantum nodes into Keras Layers
 
 *Author: Tom Bromley — Posted: 02 November 2020. Last updated: 28 January 2021.*
 
@@ -37,7 +37,7 @@
 # .. note::
 #
 #     A similar demo explaining how to
-#     :doc:`turn quantum nodes into Keras layers <tutorial_qnn_module_tf>`
+#     :doc:`turn quantum nodes into Keras layers <qnn_module_tf>`
 #     is also available.
 #
 # Fixing the dataset and problem
diff --git a/demonstrations/tutorial_testing_symmetry.metadata.json b/demonstrations/tutorial_testing_symmetry.metadata.json
index 2a7ca1c3df..b42d1b3179 100644
--- a/demonstrations/tutorial_testing_symmetry.metadata.json
+++ b/demonstrations/tutorial_testing_symmetry.metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-01-24T00:00:00+00:00",
-    "dateOfLastModification": "2024-11-06T00:00:00+00:00",
+    "dateOfLastModification": "2025-01-23T00:00:00+00:00",
     "categories": [
         "Algorithms",
         "Quantum Computing"
diff --git a/demonstrations/tutorial_testing_symmetry.py b/demonstrations/tutorial_testing_symmetry.py
index 61e0776885..802ac04055 100644
--- a/demonstrations/tutorial_testing_symmetry.py
+++ b/demonstrations/tutorial_testing_symmetry.py
@@ -308,14 +308,14 @@ def prep_plus():
 
 # Implement controlled symmetry operations on system
 def CU_sys():
-    qml.ControlledQubitUnitary(c_mat @ c_mat, control_wires=[aux[0]], wires=system)
-    qml.ControlledQubitUnitary(c_mat, control_wires=[aux[1]], wires=system)
+    qml.ControlledQubitUnitary(c_mat @ c_mat, wires=[aux[0]] + list(system))
+    qml.ControlledQubitUnitary(c_mat, wires=[aux[1]] + list(system))
 
 
 # Implement controlled symmetry operations on copy
 def CU_cpy():
-    qml.ControlledQubitUnitary(c_mat @ c_mat, control_wires=[aux[0]], wires=copy)
-    qml.ControlledQubitUnitary(c_mat, control_wires=[aux[1]], wires=copy)
+    qml.ControlledQubitUnitary(c_mat @ c_mat, wires=[aux[0]] + list(copy))
+    qml.ControlledQubitUnitary(c_mat, wires=[aux[1]] + list(copy))
 
 ######################################################################
 # Let’s combine everything and actually run our circuit!
diff --git a/demonstrations/tutorial_zne_catalyst.metadata.json b/demonstrations/tutorial_zne_catalyst.metadata.json
index f9cc710683..a2cd39b777 100644
--- a/demonstrations/tutorial_zne_catalyst.metadata.json
+++ b/demonstrations/tutorial_zne_catalyst.metadata.json
@@ -9,7 +9,7 @@
         }
     ],
     "dateOfPublication": "2024-11-15T00:00:00+00:00",
-    "dateOfLastModification": "2024-11-25T09:00:00+00:00",
+    "dateOfLastModification": "2025-04-11T09:00:00+00:00",
     "categories": [
         "Algorithms",
         "Quantum Computing"
diff --git a/demonstrations/tutorial_zne_catalyst.py b/demonstrations/tutorial_zne_catalyst.py
index 0addd8d542..847360513f 100644
--- a/demonstrations/tutorial_zne_catalyst.py
+++ b/demonstrations/tutorial_zne_catalyst.py
@@ -85,6 +85,7 @@
 #      pip install -U pennylane-catalyst pennylane-qrack 
 # 
 
+
 ##############################################################################
 # Defining the mirror circuit
 # ---------------------------
diff --git a/demonstrations_v2/tutorial_qnn_module_tf/demo.py b/demonstrations_v2/qnn_module_tf/demo.py
similarity index 81%
rename from demonstrations_v2/tutorial_qnn_module_tf/demo.py
rename to demonstrations_v2/qnn_module_tf/demo.py
index 8f8c906c1c..cf4237416c 100644
--- a/demonstrations_v2/tutorial_qnn_module_tf/demo.py
+++ b/demonstrations_v2/qnn_module_tf/demo.py
@@ -1,4 +1,7 @@
-"""
+r"""
+.. role:: html(raw)
+    :format: html
+
 Turning quantum nodes into Keras Layers
 =======================================
 
@@ -10,7 +13,13 @@
 
    tutorial_qnn_module_torch Turning quantum nodes into Torch Layers
 
-*Author: Tom Bromley — Posted: 02 November 2020. Last updated: 28 January 2021.*
+*Author: Tom Bromley — Posted: 02 November 2020. Last updated: 21 March 2025.*
+
+.. warning::
+
+    This demo is only compatible with PennyLane version 0.40 or below.
+    For usage with a later version of PennyLane, please consider using
+    :doc:`PyTorch <tutorial_qnn_module_torch>` or :doc:`JAX <tutorial_How_to_optimize_QML_model_using_JAX_and_Optax>`.
 
 Creating neural networks in `Keras <https://keras.io/>`__ is easy. Models are constructed from
 elementary *layers* and can be trained using a high-level API. For example, the following code
@@ -67,6 +76,11 @@
 plt.scatter(X[:, 0], X[:, 1], c=c)
 plt.show()
 
+##############################################################################
+# .. figure:: /_static/demonstration_assets/qnn_module/sphx_glr_qnn_module_tf_001.png
+#    :width: 100%
+#    :align: center
+
 ###############################################################################
 # Defining a QNode
 # ----------------
@@ -166,7 +180,25 @@ def qnode(inputs, weights):
 
 fitting = model.fit(X, y_hot, epochs=6, batch_size=5, validation_split=0.25, verbose=2)
 
-###############################################################################
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#  .. code-block:: none
+#
+#    Epoch 1/6
+#    30/30 - 4s - loss: 0.4153 - accuracy: 0.7333 - val_loss: 0.3183 - val_accuracy: 0.7800 - 4s/epoch - 123ms/step
+#    Epoch 2/6
+#    30/30 - 4s - loss: 0.2927 - accuracy: 0.8000 - val_loss: 0.2475 - val_accuracy: 0.8400 - 4s/epoch - 130ms/step
+#    Epoch 3/6
+#    30/30 - 4s - loss: 0.2272 - accuracy: 0.8333 - val_loss: 0.2111 - val_accuracy: 0.8200 - 4s/epoch - 119ms/step
+#    Epoch 4/6
+#    30/30 - 4s - loss: 0.1963 - accuracy: 0.8667 - val_loss: 0.1917 - val_accuracy: 0.8600 - 4s/epoch - 118ms/step
+#    Epoch 5/6
+#    30/30 - 4s - loss: 0.1772 - accuracy: 0.8667 - val_loss: 0.1828 - val_accuracy: 0.8600 - 4s/epoch - 117ms/step
+#    Epoch 6/6
+#    30/30 - 4s - loss: 0.1603 - accuracy: 0.8733 - val_loss: 0.2006 - val_accuracy: 0.8200 - 4s/epoch - 117ms/step
+#
+#
 # How did we do? The model looks to have successfully trained and the accuracy on both the
 # training and validation datasets is reasonably high. In practice, we would aim to push the
 # accuracy higher by thinking carefully about the model design and the choice of hyperparameters
@@ -224,7 +256,25 @@ def qnode(inputs, weights):
 
 fitting = model.fit(X, y_hot, epochs=6, batch_size=5, validation_split=0.25, verbose=2)
 
-###############################################################################
+##############################################################################
+# .. rst-class:: sphx-glr-script-out
+#
+#  .. code-block:: none
+#
+#    Epoch 1/6
+#    30/30 - 7s - loss: 0.3682 - accuracy: 0.7467 - val_loss: 0.2550 - val_accuracy: 0.8000 - 7s/epoch - 229ms/step
+#    Epoch 2/6
+#    30/30 - 7s - loss: 0.2428 - accuracy: 0.8067 - val_loss: 0.2105 - val_accuracy: 0.8400 - 7s/epoch - 224ms/step
+#    Epoch 3/6
+#    30/30 - 7s - loss: 0.2001 - accuracy: 0.8333 - val_loss: 0.1901 - val_accuracy: 0.8200 - 7s/epoch - 220ms/step
+#    Epoch 4/6
+#    30/30 - 7s - loss: 0.1816 - accuracy: 0.8600 - val_loss: 0.1776 - val_accuracy: 0.8200 - 7s/epoch - 224ms/step
+#    Epoch 5/6
+#    30/30 - 7s - loss: 0.1661 - accuracy: 0.8667 - val_loss: 0.1711 - val_accuracy: 0.8600 - 7s/epoch - 224ms/step
+#    Epoch 6/6
+#    30/30 - 7s - loss: 0.1520 - accuracy: 0.8600 - val_loss: 0.1807 - val_accuracy: 0.8200 - 7s/epoch - 221ms/step
+#
+#
 # Great! We've mastered the basics of constructing hybrid classical-quantum models using
 # PennyLane and Keras. Can you think of any interesting hybrid models to construct? How do they
 # perform on realistic datasets?
diff --git a/demonstrations_v2/tutorial_qnn_module_tf/metadata.json b/demonstrations_v2/qnn_module_tf/metadata.json
similarity index 93%
rename from demonstrations_v2/tutorial_qnn_module_tf/metadata.json
rename to demonstrations_v2/qnn_module_tf/metadata.json
index 36a7aab856..15d489d26a 100644
--- a/demonstrations_v2/tutorial_qnn_module_tf/metadata.json
+++ b/demonstrations_v2/qnn_module_tf/metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2020-11-02T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-21T00:00:00+00:00",
     "categories": [
         "Devices and Performance",
         "Quantum Machine Learning"
diff --git a/demonstrations_v2/tutorial_qnn_module_tf/requirements.in b/demonstrations_v2/qnn_module_tf/requirements.in
similarity index 100%
rename from demonstrations_v2/tutorial_qnn_module_tf/requirements.in
rename to demonstrations_v2/qnn_module_tf/requirements.in
diff --git a/demonstrations_v2/tutorial_kak_decomposition/demo.py b/demonstrations_v2/tutorial_kak_decomposition/demo.py
index a4789f8a4e..8a61aa3cc9 100644
--- a/demonstrations_v2/tutorial_kak_decomposition/demo.py
+++ b/demonstrations_v2/tutorial_kak_decomposition/demo.py
@@ -355,7 +355,7 @@ def check_cartan_decomposition(g, k, space_name):
     """Given an algebra g and an operator subspace k, verify that k is a subalgebra
     and gives rise to a Cartan decomposition."""
     # Check Lie closure of k
-    k_lie_closure = qml.pauli.dla.lie_closure(k)
+    k_lie_closure = qml.lie_closure(k)
     k_is_closed = len(k_lie_closure) == len(k)
     print(f"The Lie closure of k is as big as k itself: {k_is_closed}.")
 
diff --git a/demonstrations_v2/tutorial_kak_decomposition/metadata.json b/demonstrations_v2/tutorial_kak_decomposition/metadata.json
index 94e43e6012..ae7f442f81 100644
--- a/demonstrations_v2/tutorial_kak_decomposition/metadata.json
+++ b/demonstrations_v2/tutorial_kak_decomposition/metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-11-25T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-07T09:00:00+00:00",
+    "dateOfLastModification": "2025-03-17T09:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Algorithms"
diff --git a/demonstrations_v2/tutorial_liesim/demo.py b/demonstrations_v2/tutorial_liesim/demo.py
index 14f91b9a05..20630ee3da 100644
--- a/demonstrations_v2/tutorial_liesim/demo.py
+++ b/demonstrations_v2/tutorial_liesim/demo.py
@@ -186,7 +186,7 @@
 # work with PauliSentence instances for efficiency
 generators = [op.pauli_rep for op in generators]
 
-dla = qml.pauli.lie_closure(generators, pauli=True)
+dla = qml.lie_closure(generators, pauli=True)
 dim_g = len(dla)
 
 ##############################################################################
@@ -239,7 +239,7 @@
 # the forward pass of the expectation value computation. For demonstration purposes,
 # we choose a random subset of ``depth=10`` generators for gates from the DLA.
 
-adjoint_repr = qml.pauli.structure_constants(dla)
+adjoint_repr = qml.structure_constants(dla)
 
 depth = 10
 gate_choice = np.random.choice(dim_g, size=depth)
diff --git a/demonstrations_v2/tutorial_liesim/metadata.json b/demonstrations_v2/tutorial_liesim/metadata.json
index f3d75d959d..a307de56e6 100644
--- a/demonstrations_v2/tutorial_liesim/metadata.json
+++ b/demonstrations_v2/tutorial_liesim/metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-06-07T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-17T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Getting Started"
diff --git a/demonstrations_v2/tutorial_liesim_extension/demo.py b/demonstrations_v2/tutorial_liesim_extension/demo.py
index b11a640b6e..ce62806eb9 100644
--- a/demonstrations_v2/tutorial_liesim_extension/demo.py
+++ b/demonstrations_v2/tutorial_liesim_extension/demo.py
@@ -99,7 +99,7 @@ def TFIM(n):
     generators += [Z(i) for i in range(n)]
     generators = [op.pauli_rep for op in generators]
 
-    dla = qml.pauli.lie_closure(generators, pauli=True)
+    dla = qml.lie_closure(generators, pauli=True)
     dim_dla = len(dla)
     return generators, dla, dim_dla
 
diff --git a/demonstrations_v2/tutorial_liesim_extension/metadata.json b/demonstrations_v2/tutorial_liesim_extension/metadata.json
index 7da2c15c82..3837196b18 100644
--- a/demonstrations_v2/tutorial_liesim_extension/metadata.json
+++ b/demonstrations_v2/tutorial_liesim_extension/metadata.json
@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2024-06-18T00:00:00+00:00",
-    "dateOfLastModification": "2025-01-13T00:00:00+00:00",
+    "dateOfLastModification": "2025-03-17T00:00:00+00:00",
     "categories": [
         "Quantum Computing",
         "Quantum Machine Learning"
diff --git a/demonstrations_v2/tutorial_qnn_module_torch/demo.py b/demonstrations_v2/tutorial_qnn_module_torch/demo.py
index f3f59a77ff..253d749eca 100644
--- a/demonstrations_v2/tutorial_qnn_module_torch/demo.py
+++ b/demonstrations_v2/tutorial_qnn_module_torch/demo.py
@@ -8,7 +8,7 @@
 
 .. related::
 
-   tutorial_qnn_module_tf Turning quantum nodes into Keras Layers
+   qnn_module_tf Turning quantum nodes into Keras Layers
 
 *Author: Tom Bromley — Posted: 02 November 2020. Last updated: 28 January 2021.*
 
@@ -37,7 +37,7 @@
 # .. note::
 #
 #     A similar demo explaining how to
-#     :doc:`turn quantum nodes into Keras layers <tutorial_qnn_module_tf>`
+#     :doc:`turn quantum nodes into Keras layers <qnn_module_tf>`
 #     is also available.
 #
 # Fixing the dataset and problem
diff --git a/demonstrations_v2/tutorial_qnn_module_torch/metadata.json b/demonstrations_v2/tutorial_qnn_module_torch/metadata.json
index bfd569d229..f7e3189dc6 100644
--- a/demonstrations_v2/tutorial_qnn_module_torch/metadata.json
+++ b/demonstrations_v2/tutorial_qnn_module_torch/metadata.json
@@ -26,7 +26,7 @@
     "relatedContent": [
         {
             "type": "demonstration",
-            "id": "tutorial_qnn_module_tf",
+            "id": "qnn_module_tf",
             "weight": 1.0
         }
     ]
diff --git a/poetry.lock b/poetry.lock
index d497c996b5..eff89c4dac 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -661,138 +661,46 @@ numpy = ">=1.24.1"
 toolz = ">=0.9.0"
 typing_extensions = ">=4.2.0"
 
-[[package]]
-name = "cirq"
-version = "1.0.0"
-description = "A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq-1.0.0-py3-none-any.whl", hash = "sha256:8d61feba1c8d2a37d82a53e2500fb2430ad8e0ca7c3e2562fbd554bea0d9d7f7"},
-]
-
-[package.dependencies]
-cirq-aqt = "1.0.0"
-cirq-core = "1.0.0"
-cirq-google = "1.0.0"
-cirq-ionq = "1.0.0"
-cirq-pasqal = "1.0.0"
-cirq-rigetti = "1.0.0"
-cirq-web = "1.0.0"
-
-[package.extras]
-dev-env = ["asv", "black (==22.3.0)", "codeowners", "coverage (<=6.2)", "dill (==0.3.4)", "filelock (>=3.0.12,<3.1.0)", "flynt (>=0.60,<1.0)", "freezegun (>=0.3.15,<0.4.0)", "grpcio-tools (>=1.37.0,<1.38.0)", "importlib-metadata", "ipykernel (==5.3.4)", "ipython", "ipython (==7.31.1)", "mypy (==0.961.0)", "mypy-protobuf (==1.10)", "notebook (>=6.4.1,<=6.4.7)", "papermill (>=2.3.2,<2.4.0)", "pylint (>=2.13.0,<2.14.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-xdist (>=2.2.0,<2.3.0)", "qiskit-aer (>=0.10.4,<0.11.0)", "rstcheck (>=3.3.1,<3.4.0)", "seaborn (>=0.11.1,<0.12.0)", "setuptools", "twine", "types-backports (==0.1.3)", "types-protobuf (==3.19.22)", "types-requests (==2.28.1)", "types-setuptools (==62.6.1)", "virtualenv", "virtualenv-clone", "wheel"]
-
-[[package]]
-name = "cirq-aqt"
-version = "1.0.0"
-description = "A Cirq package to simulate and connect to Alpine Quantum Technologies quantum computers"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq_aqt-1.0.0-py3-none-any.whl", hash = "sha256:db7af0b40ceded8a5951f74f36e2235a873df519078e069a6e4d00fda4acd82d"},
-]
-
-[package.dependencies]
-cirq-core = "1.0.0"
-requests = ">=2.18,<3.0"
-
 [[package]]
 name = "cirq-core"
-version = "1.0.0"
+version = "1.4.1"
 description = "A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits."
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.10.0"
 files = [
-    {file = "cirq_core-1.0.0-py3-none-any.whl", hash = "sha256:a1df552e99ab339121c40c7bf4d18ba542bba151dd8b5ac8c4c33b7b9d46ba10"},
+    {file = "cirq_core-1.4.1-py3-none-any.whl", hash = "sha256:869db60413265c41a8206854c1d4ca9bad5fac9cfd7c6a10685b5a6d516defa0"},
 ]
 
 [package.dependencies]
-duet = ">=0.2.7,<0.3.0"
+attrs = ">=21.3.0"
+duet = ">=0.2.8"
 matplotlib = ">=3.0,<4.0"
-networkx = ">=2.4,<3.0"
-numpy = ">=1.16,<2.0"
+networkx = ">=2.4"
+numpy = ">=1.22,<2.0"
 pandas = "*"
-scipy = "*"
+scipy = ">=1.0,<2.0"
 sortedcontainers = ">=2.0,<3.0"
 sympy = "*"
 tqdm = "*"
-typing-extensions = "*"
+typing-extensions = ">=4.2"
 
 [package.extras]
-contrib = ["autoray", "numba (>=0.53.0)", "opt-einsum", "ply (>=3.4)", "pylatex (>=1.3.0,<1.4.0)", "quimb"]
-
-[[package]]
-name = "cirq-google"
-version = "1.0.0"
-description = "The Cirq module that provides tools and access to the Google Quantum Computing Service"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq_google-1.0.0-py3-none-any.whl", hash = "sha256:8fd1df0fd97d1ee076c01a2b2ab86aac966692890fb2793a45ef2393a6ccc4ab"},
-]
-
-[package.dependencies]
-cirq-core = "1.0.0"
-google-api-core = {version = ">=1.14.0,<2.0.0dev", extras = ["grpc"]}
-proto-plus = ">=1.20.0"
-protobuf = ">=3.15.0,<4"
-
-[[package]]
-name = "cirq-ionq"
-version = "1.0.0"
-description = "A Cirq package to simulate and connect to IonQ quantum computers"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq_ionq-1.0.0-py3-none-any.whl", hash = "sha256:31ff254a5b514736f79a92a186d473b3e40550c94ad36aeebddea1cb840fb065"},
-]
-
-[package.dependencies]
-cirq-core = "1.0.0"
-requests = ">=2.18,<3.0"
+contrib = ["opt-einsum", "ply (>=3.6)", "pylatex (>=1.4,<2.0)", "quimb (>=1.7,<2.0)"]
 
 [[package]]
 name = "cirq-pasqal"
-version = "1.0.0"
+version = "1.4.1"
 description = "A Cirq package to simulate and connect to Pasqal quantum computers"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.10.0"
 files = [
-    {file = "cirq_pasqal-1.0.0-py3-none-any.whl", hash = "sha256:1243aae6514aadb6a5e2f729da3d99904d20631e7d2a7fbaef69c7377a9158b5"},
+    {file = "cirq_pasqal-1.4.1-py3-none-any.whl", hash = "sha256:eb0cdad9f33159e436f38bf761bbacaebebb70c0a3a3bd0e7dfb08d079869489"},
 ]
 
 [package.dependencies]
-cirq-core = "1.0.0"
+cirq-core = "1.4.1"
 requests = ">=2.18,<3.0"
 
-[[package]]
-name = "cirq-rigetti"
-version = "1.0.0"
-description = "A Cirq package to simulate and connect to Rigetti quantum computers and Quil QVM"
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq_rigetti-1.0.0-py3-none-any.whl", hash = "sha256:a0ff2ac5dea2b175ced45ade23bd2bfc2f1d8d280b6a003b1dcdddfdd579c0bd"},
-]
-
-[package.dependencies]
-cirq-core = "1.0.0"
-pyquil = ">=3.0.0"
-
-[[package]]
-name = "cirq-web"
-version = "1.0.0"
-description = "Web-based 3D visualization tools for Cirq."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "cirq_web-1.0.0-py3-none-any.whl", hash = "sha256:e3ebc6043b5a6a2a4ba57a71ea9ea5bc487732527b4512961e4c4ce90b2f5744"},
-]
-
-[package.dependencies]
-cirq-core = "1.0.0"
-
 [[package]]
 name = "clarabel"
 version = "0.10.0"
@@ -1297,23 +1205,6 @@ files = [
     {file = "decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360"},
 ]
 
-[[package]]
-name = "deprecated"
-version = "1.2.18"
-description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
-files = [
-    {file = "Deprecated-1.2.18-py2.py3-none-any.whl", hash = "sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec"},
-    {file = "deprecated-1.2.18.tar.gz", hash = "sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d"},
-]
-
-[package.dependencies]
-wrapt = ">=1.10,<2"
-
-[package.extras]
-dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
-
 [[package]]
 name = "deprecation"
 version = "2.1.0"
@@ -2447,30 +2338,6 @@ files = [
     {file = "gast-0.6.0.tar.gz", hash = "sha256:88fc5300d32c7ac6ca7b515310862f71e6fdf2c029bbec7c66c0f5dd47b6b1fb"},
 ]
 
-[[package]]
-name = "google-api-core"
-version = "1.34.1"
-description = "Google API client core library"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "google-api-core-1.34.1.tar.gz", hash = "sha256:3399c92887a97d33038baa4bfd3bf07acc05d474b0171f333e1f641c1364e552"},
-    {file = "google_api_core-1.34.1-py3-none-any.whl", hash = "sha256:52bcc9d9937735f8a3986fa0bbf9135ae9cf5393a722387e5eced520e39c774a"},
-]
-
-[package.dependencies]
-google-auth = ">=1.25.0,<3.0dev"
-googleapis-common-protos = ">=1.56.2,<2.0dev"
-grpcio = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""}
-grpcio-status = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""}
-protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.0.0dev"
-requests = ">=2.18.0,<3.0.0dev"
-
-[package.extras]
-grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)"]
-grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
-grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"]
-
 [[package]]
 name = "google-auth"
 version = "2.38.0"
@@ -2528,23 +2395,6 @@ files = [
 [package.dependencies]
 six = "*"
 
-[[package]]
-name = "googleapis-common-protos"
-version = "1.68.0"
-description = "Common protobufs used in Google APIs"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "googleapis_common_protos-1.68.0-py2.py3-none-any.whl", hash = "sha256:aaf179b2f81df26dfadac95def3b16a95064c76a5f45f07e4c68a21bb371c4ac"},
-    {file = "googleapis_common_protos-1.68.0.tar.gz", hash = "sha256:95d38161f4f9af0d9423eed8fb7b64ffd2568c3464eb542ff02c5bfa1953ab3c"},
-]
-
-[package.dependencies]
-protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
-
-[package.extras]
-grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
-
 [[package]]
 name = "greenlet"
 version = "3.1.1"
@@ -2698,22 +2548,6 @@ files = [
 [package.extras]
 protobuf = ["grpcio-tools (>=1.70.0)"]
 
-[[package]]
-name = "grpcio-status"
-version = "1.48.2"
-description = "Status proto mapping for gRPC"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "grpcio-status-1.48.2.tar.gz", hash = "sha256:53695f45da07437b7c344ee4ef60d370fd2850179f5a28bb26d8e2aa1102ec11"},
-    {file = "grpcio_status-1.48.2-py3-none-any.whl", hash = "sha256:2c33bbdbe20188b2953f46f31af669263b6ee2a9b2d38fa0d36ee091532e21bf"},
-]
-
-[package.dependencies]
-googleapis-common-protos = ">=1.5.5"
-grpcio = ">=1.48.2"
-protobuf = ">=3.12.0"
-
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -3394,21 +3228,6 @@ files = [
     {file = "kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e"},
 ]
 
-[[package]]
-name = "lark"
-version = "0.11.3"
-description = "a modern parsing library"
-optional = false
-python-versions = "*"
-files = [
-    {file = "lark-0.11.3.tar.gz", hash = "sha256:3100d9749b5a85735ec428b83100876a5da664804579e729c23a36341f961e7e"},
-]
-
-[package.extras]
-atomic-cache = ["atomicwrites"]
-nearley = ["js2py"]
-regex = ["regex"]
-
 [[package]]
 name = "libclang"
 version = "18.1.1"
@@ -3861,23 +3680,29 @@ scipy = ">=1.7.3"
 
 [[package]]
 name = "mitiq"
-version = "0.32.0"
+version = "0.43.0"
 description = "Mitiq is an open source toolkit for implementing error mitigation techniques on most current intermediate-scale quantum computers."
 optional = false
-python-versions = ">=3.9,<3.12"
+python-versions = "<3.13,>=3.10"
 files = [
-    {file = "mitiq-0.32.0-py3-none-any.whl", hash = "sha256:8f61880383931aae93bc108591543a967185a54dbc89ed1faf5dd30a7b8f59ca"},
-    {file = "mitiq-0.32.0.tar.gz", hash = "sha256:b44a9257055d4ef5d17ccfb211f1a4fcb3722139b5e48d795e13501470d1d7ef"},
+    {file = "mitiq-0.43.0-py3-none-any.whl", hash = "sha256:3af95694a173bd1a1e3558769dc4d2aa7552b0e12967c1f83f2a6b3d90191442"},
+    {file = "mitiq-0.43.0.tar.gz", hash = "sha256:e2db3a68dafcbd06c616d8a1ed9f9a7e70e2a1b0b99cd826e124790fdd6bf575"},
 ]
 
 [package.dependencies]
-cirq = ">=1.0.0,<1.4.0"
+cirq-core = ">=1.4.0,<1.5.0"
 numpy = ">=1.22.0"
-scipy = ">=1.5.0,<=1.11.4"
+scipy = ">=1.10.1,<=1.14.1"
 tabulate = "*"
 
 [package.extras]
-development = ["Sphinx (==5.2.3)", "amazon-braket-sdk (>=1.64.1,<1.65.0)", "black (==22.10)", "bqskit[ext] (==1.0.4)", "flake8 (==6.0.0)", "isort (==5.13.2)", "jupytext (==1.14.1)", "matplotlib (==3.8.1)", "mypy (==1.0.0)", "myst-nb (==1.0.0)", "nbsphinx (==0.9.1)", "openfermion (==1.6.0)", "openfermionpyscf (==0.5)", "pandas (==2.1.3)", "pennylane (>=0.33.1,<0.34.0)", "pennylane-qiskit (>=0.33.1,<0.34.0)", "pydata-sphinx-theme (==0.11.0)", "pyquil (>=3.5.4,<3.6.0)", "pyscf (==2.4.0)", "pytest (==7.1.3)", "pytest-cov (==4.0.0)", "pytest-xdist[psutil] (==3.0.2)", "qiskit (>=0.45.1,<0.46.0)", "qiskit-aer (>=0.13.1,<0.14.0)", "qiskit-ibm-provider (>=0.7.3,<0.8.0)", "seaborn (==0.13.0)", "sphinx-autodoc-typehints (==1.19.4)", "sphinx-copybutton (==0.5.0)", "sphinx-gallery (==0.10.1)", "sphinxcontrib-bibtex (==2.5.0)", "stim (==1.12.1)", "stimcirq (==1.12.1)", "types-tabulate"]
+braket = ["amazon-braket-sdk (>=1.69.0,<1.70.0)", "cirq-ionq (>=1.4.0,<1.5.0)"]
+cirq = ["cirq-core (>=1.4.0,<1.5.0)"]
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 typing-extensions = ">=3.7.4"
 
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 [package.extras]
 iqm = ["qiskit-iqm"]
 
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@@ -6770,46 +6277,42 @@ test = ["openfermion"]
 
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+    {file = "scipy-1.14.1.tar.gz", hash = "sha256:5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "scipy-openblas32"
@@ -8130,21 +7630,6 @@ files = [
 [package.dependencies]
 tqdm = "*"
 
-[[package]]
-name = "tenacity"
-version = "8.5.0"
-description = "Retry code until it succeeds"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "tenacity-8.5.0-py3-none-any.whl", hash = "sha256:b594c2a5945830c267ce6b79a166228323ed52718f30302c1359836112346687"},
-    {file = "tenacity-8.5.0.tar.gz", hash = "sha256:8bc6c0c8a09b31e6cad13c47afbed1a567518250a9a171418582ed8d9c20ca78"},
-]
-
-[package.extras]
-doc = ["reno", "sphinx"]
-test = ["pytest", "tornado (>=4.5)", "typeguard"]
-
 [[package]]
 name = "tensorboard"
 version = "2.14.1"
@@ -8744,17 +8229,6 @@ rich = ">=10.11.0"
 shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
-[[package]]
-name = "types-deprecated"
-version = "1.2.15.20241117"
-description = "Typing stubs for Deprecated"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "types-Deprecated-1.2.15.20241117.tar.gz", hash = "sha256:924002c8b7fddec51ba4949788a702411a2e3636cd9b2a33abd8ee119701d77e"},
-    {file = "types_Deprecated-1.2.15.20241117-py3-none-any.whl", hash = "sha256:a0cc5e39f769fc54089fd8e005416b55d74aa03f6964d2ed1a0b0b2e28751884"},
-]
-
 [[package]]
 name = "types-python-dateutil"
 version = "2.9.0.20241206"
@@ -8766,17 +8240,6 @@ files = [
     {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"},
 ]
 
-[[package]]
-name = "types-retry"
-version = "0.9.9.20241221"
-description = "Typing stubs for retry"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "types_retry-0.9.9.20241221-py3-none-any.whl", hash = "sha256:d1ef1a60573470525e65267192dd712b93f0f0acf3019c4c1afe173cde3289cb"},
-    {file = "types_retry-0.9.9.20241221.tar.gz", hash = "sha256:ebad6d495a5a04ab0d06d4156a665528c3b84a8461aa019dd6e5d3e33c2aa1e0"},
-]
-
 [[package]]
 name = "types-setuptools"
 version = "75.8.0.20250225"
@@ -9612,4 +9075,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "~3.10.0"
-content-hash = "6993eb4c5db719f7a8b7867330651d516288f1c5f146540669752ac413c87160"
+content-hash = "7d4617176755cc00f125191fe224018929d4a0ed4104856d4616194e9074da9f"
diff --git a/pyproject.toml b/pyproject.toml
index ec660b8dd0..1a4f9b3b9c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -61,6 +61,7 @@ pennylane-qulacs = "0.40.0"
 pennylane-catalyst = "0.10.0"
 ##########################################################
 
+scipy = ">=1.12"
 numpy = "~1.24"
 matplotlib = "3.7.2"
 jax = "0.4.28"
@@ -79,8 +80,8 @@ pydantic = "^2.8.2"
 ply = "3.11"
 optax = "0.2.3"
 flax = "0.9.0"
-qutip = "4.7.3"
-mitiq = "0.32.0"
+qutip = "5.1.0"
+mitiq = "0.43.0"
 pennylane-qrack = "0.11.1"
 pyqrack = "1.32.12"
 zstd = "*"