diff --git a/demonstrations/tutorial_qaoa_intro.py b/demonstrations/tutorial_qaoa_intro.py
index 7caed2f250..befd317d91 100644
--- a/demonstrations/tutorial_qaoa_intro.py
+++ b/demonstrations/tutorial_qaoa_intro.py
@@ -76,7 +76,7 @@
 #     :align: center
 #     :width: 70%
 #
-# In PennyLane, this is implemented using the :func:`~.pennylane.templates.ApproxTimeEvolution`
+# In PennyLane, this is implemented using the :func:`~.pennylane.ApproxTimeEvolution`
 # template. For example, let's say we have the following Hamiltonian:
 
 import pennylane as qml
@@ -284,9 +284,9 @@ def circuit(params, **kwargs):
 #     :align: center
 #     :width: 90%
 #
-# While it is possible to use :func:`~.pennylane.templates.ApproxTimeEvolution`, the QAOA module allows you to
-# build the cost and mixer layers directly using the functions :func:`~.pennylane.qaoa.cost_layer` and
-# :func:`~.pennylane.qaoa.mixer_layer`, which take as input the respective Hamiltonian and variational parameters:
+# While it is possible to use :func:`~.pennylane.ApproxTimeEvolution`, the QAOA module allows you to
+# build the cost and mixer layers directly using the functions :func:`~.pennylane.qaoa.layers.cost_layer` and
+# :func:`~.pennylane.qaoa.layers.mixer_layer`, which take as input the respective Hamiltonian and variational parameters:
 
 
 def qaoa_layer(gamma, alpha):
@@ -424,7 +424,7 @@ def probability_circuit(gamma, alpha):
 # favour :math:`|10\rangle,` making it the only true ground state.
 #
 # It is easy to introduce constraints of this form in PennyLane.
-# We can use the :func:`~.pennylane.qaoa.edge_driver` cost
+# We can use the :func:`~.pennylane.qaoa.cost.edge_driver` cost
 # Hamiltonian to "reward" cases in which the first and last vertices of the graph
 # are :math:`0:`
 
diff --git a/demonstrations/tutorial_vqe.py b/demonstrations/tutorial_vqe.py
index 5795b87464..50203b7785 100644
--- a/demonstrations/tutorial_vqe.py
+++ b/demonstrations/tutorial_vqe.py
@@ -133,7 +133,7 @@
 # tutorial :doc:`tutorial_givens_rotations`.
 #
 # Implementing the circuit above using PennyLane is straightforward. First, we use the
-# :func:`hf_state` function to generate the vector representing the Hartree-Fock state.
+# :func:`~.pennylane.qchem.hf_state` function to generate the vector representing the Hartree-Fock state.
 
 electrons = 2
 hf = qml.qchem.hf_state(electrons, qubits)
@@ -144,7 +144,7 @@
 # the qubit register. Then, we just act with the :class:`~.pennylane.DoubleExcitation` operation
 # on the four qubits. The next step is to compute the expectation value
 # of the molecular Hamiltonian in the trial state prepared by the circuit.
-# We do this using the :func:`~.expval` function. The decorator syntax allows us to
+# We do this using the :func:`~.pennylane.expval` function. The decorator syntax allows us to
 # run the cost function as an executable QNode with the gate parameter :math:`\theta:`
 
 @qml.qnode(dev, interface="jax")
@@ -265,7 +265,7 @@ def cost_fn(param):
 # molecular Hamiltonian in the trial state.
 #
 # The VQE algorithm can be used to simulate other chemical phenomena.
-# In the tutorial :doc:`tutorial_vqe_bond_dissociation`, we use VQE to explore the
+# In the tutorial :doc:`tutorial_chemical_reactions`, we use VQE to explore the
 # potential energy surface of molecules to simulate chemical reactions.
 # Another interesting application is to probe the lowest-lying states of molecules
 # in specific sectors of the Hilbert space. For example, see the tutorial