diff --git a/qiskit/algorithms/minimum_eigen_solvers/vqe.py b/qiskit/algorithms/minimum_eigen_solvers/vqe.py
index 29d10b7be31e..87ab8707b0f1 100755
--- a/qiskit/algorithms/minimum_eigen_solvers/vqe.py
+++ b/qiskit/algorithms/minimum_eigen_solvers/vqe.py
@@ -92,7 +92,7 @@ class VQE(VariationalAlgorithm, MinimumEigensolver):
         The callable _must_ have the argument names ``fun, x0, jac, bounds`` as indicated
         in the following code block.
 
-    .. code-block::python
+    .. code-block:: python
 
         from qiskit.algorithms.optimizers import OptimizerResult
 
@@ -111,7 +111,7 @@ def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
 
     The above signature also allows to directly pass any SciPy minimizer, for instance as
 
-    .. code-block::python
+    .. code-block:: python
 
         from functools import partial
         from scipy.optimize import minimize
diff --git a/qiskit/algorithms/optimizers/gradient_descent.py b/qiskit/algorithms/optimizers/gradient_descent.py
index 5ca2e0f8cad8..38ed55048623 100644
--- a/qiskit/algorithms/optimizers/gradient_descent.py
+++ b/qiskit/algorithms/optimizers/gradient_descent.py
@@ -50,7 +50,7 @@ class GradientDescent(Optimizer):
         A minimum example that will use finite difference gradients with a default perturbation
         of 0.01 and a default learning rate of 0.01.
 
-        .. code-block::python
+        .. code-block:: python
 
             from qiskit.algorithms.optimizers import GradientDescent
 
@@ -70,7 +70,7 @@ def f(x):
         Note how much faster this convergences (i.e. less ``nfevs``) compared to the previous
         example.
 
-        .. code-block::python
+        .. code-block:: python
 
             from qiskit.algorithms.optimizers import GradientDescent