fix: python function definition helper texts of directory qiskit/algorithms/optimizers/ (backport #11080)

qiskit/algorithms/optimizers/gradient_descent.py

@@ -197,7 +197,7 @@ def __init__(
                 perturbation in both directions (defaults to 1e-2 if required).
                 Ignored when we have an explicit function for the gradient.
         Raises:
-            ValueError: If ``learning_rate`` is an array and its lenght is less than ``maxiter``.
+            ValueError: If ``learning_rate`` is an array and its length is less than ``maxiter``.
         """
         super().__init__(maxiter=maxiter)
         self.callback = callback
@@ -250,7 +250,7 @@ def perturbation(self, perturbation: float | None) -> None:
 
     def _callback_wrapper(self) -> None:
         """
-        Wraps the callback function to accomodate GradientDescent.
+        Wraps the callback function to accommodate GradientDescent.
 
         Will call :attr:`~.callback` and pass the following arguments:
         current number of function values, current parameters, current function value,
@@ -295,7 +295,7 @@ def ask(self) -> AskData:
 
     def tell(self, ask_data: AskData, tell_data: TellData) -> None:
         """
-        Updates :attr:`.~GradientDescentState.x` by an ammount proportional to the learning
+        Updates :attr:`.~GradientDescentState.x` by an amount proportional to the learning
         rate and value of the gradient at that point.
 
         Args:

qiskit/algorithms/optimizers/scipy_optimizer.py

@@ -122,7 +122,7 @@ def minimize(
         jac: Callable[[POINT], POINT] | None = None,
         bounds: list[tuple[float, float]] | None = None,
     ) -> OptimizerResult:
-        # Remove ignored parameters to supress the warning of scipy.optimize.minimize
+        # Remove ignored parameters to suppress the warning of scipy.optimize.minimize
         if self.is_bounds_ignored:
             bounds = None
         if self.is_gradient_ignored:

qiskit/algorithms/optimizers/spsa.py

@@ -328,7 +328,7 @@ def calibrate(
         steps = 25
         points = []
         for _ in range(steps):
-            # compute the random directon
+            # compute the random direction
             pert = bernoulli_perturbation(dim)
             points += [initial_point + c * pert, initial_point - c * pert]
 

qiskit/algorithms/optimizers/steppable_optimizer.py

@@ -71,7 +71,7 @@ class OptimizerState:
     njev: int | None
     """Number of jacobian evaluations so far in the opimization."""
     nit: int | None
-    """Number of optmization steps performed so far in the optimization."""
+    """Number of optimization steps performed so far in the optimization."""
 
 
 class SteppableOptimizer(Optimizer):
@@ -81,7 +81,7 @@ class SteppableOptimizer(Optimizer):
     This family of optimizers uses the `ask and tell interface
     <https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/009_ask_and_tell.html>`_.
     When using this interface the user has to call :meth:`~.ask` to get information about
-    how to evaluate the fucntion (we are asking the optimizer about how to do the evaluation).
+    how to evaluate the function (we are asking the optimizer about how to do the evaluation).
     This information is typically the next points at which the function is evaluated, but depending
     on the optimizer it can also determine whether to evaluate the function or its gradient.
     Once the function has been evaluated, the user calls the method :meth:`~..tell`
@@ -180,7 +180,7 @@ def ask(self) -> AskData:
         It is the first method inside of a :meth:`~.step` in the optimization process.
 
         Returns:
-            An object containing the data needed to make the funciton evaluation to advance the
+            An object containing the data needed to make the function evaluation to advance the
             optimization process.
 
         """
@@ -217,7 +217,7 @@ def evaluate(self, ask_data: AskData) -> TellData:
 
     def _callback_wrapper(self) -> None:
         """
-        Wraps the callback function to accomodate each optimizer.
+        Wraps the callback function to accommodate each optimizer.
         """
         pass
 

qiskit/algorithms/optimizers/umda.py

@@ -49,7 +49,7 @@ class UMDA(Optimizer):
     have been obtained [1]. UMDA seems to provide very good solutions for those circuits in which
     the number of layers is not big.
 
-    The optimization process can be personalized depending on the paremeters chosen in the
+    The optimization process can be personalized depending on the parameters chosen in the
     initialization. The main parameter is the population size. The bigger it is, the final result
     will be better. However, this increases the complexity of the algorithm and the runtime will
     be much heavier. In the work [1] different experiments have been performed where population