Restructure line breaks

Author: edoaltamura

releasenotes/notes/qiskit-algorithms-incorporated-421554a4ff547d0d.yaml

@@ -1,28 +1,24 @@
 ---
 prelude: >
     This release includes the migration of a subset of Qiskit Algorithms features to Qiskit Machine
-    Learning.
-    This ensures continued enhancement of essential features for Qiskit Machine Learning following
-    the end of official support for Qiskit Algorithms. Therefore, Qiskit Machine Learning
-    will no longer depend on
-    Qiskit Algorithms, possibly introducing breaking changes in import structures. Some quick-
-    fixes are
-    described below.
+    Learning. This ensures continued enhancement of essential features for Qiskit Machine
+    Learning following the end of official support for Qiskit Algorithms. Therefore, Qiskit
+    Machine Learning will no longer depend on Qiskit Algorithms, possibly introducing breaking
+    changes in import structures. Some quick-fixes are described below.
 
 features:
   - |
     Migrated essential Qiskit Algorithms features to Qiskit Machine Learning:
 
     - `qiskit_algorithms/gradients` -> `qiskit_machine_learning/gradients`.
       Note:
-      only the SPSA,
-      parameter-shift and linear-combination-of-unitaries gradients are retained. Other gradient
-      strategies, such as reverse and finite-diff are not incorporated.
+      only the SPSA, parameter-shift and linear-combination-of-unitaries gradients are retained.
+      Other gradient strategies, such as reverse and finite-diff are not incorporated.
 
     - `qiskit_algorithms/optimizers` -> `qiskit_machine_learning/optimizers`.
       Note:
-      optimizers
-      from `scikit-quant <https://scikit-quant.readthedocs.io/en/latest/>`_ are not incorporated.
+      optimizers from `scikit-quant <https://scikit-quant.readthedocs.io/en/latest/>`_ are not
+      incorporated.
 
     - `qiskit_algorithms/state_fidelities` -> `qiskit_machine_learning/state_fidelities`
 
@@ -34,9 +30,8 @@ features:
 upgrade:
   - |
     The merge of some of the features of Qiskit Algorithms into Qiskit Machine Learning might lead
-    to breaking changes.
-    For this reason, caution is advised when updating to version 0.8 during critical production
-    stages in a project.
+    to breaking changes. For this reason, caution is advised when updating to version 0.8 during
+    critical production stages in a project.
 
   - |
     Users must update their imports and code references in code that uses Qiskit Machine Leaning
@@ -52,18 +47,14 @@ upgrade:
 
   - |
     To continue using sub-modules and functionalities of Qiskit Algorithms that **have not been
-    transferred**,
-    you may continue using them as before by importing from Qiskit Algorithms. However, be aware
-    that Qiskit Algorithms
-    is no longer officially supported and some of its functionalities may not work in your use case.
-    For any problems
-    directly related to Qiskit Algorithms, please open a GitHub issue at
-    https://github.com/qiskit-community/qiskit-algorithms.
-    Should you want to include a Qiskit Algorithms functionality that has not been incorporated in
-    Qiskit Machine Learning,
-    please open a feature-request issue at
-    https://github.com/qiskit-community/qiskit-machine-learning, explaining why
-    this change would be useful for you and other users.
+    transferred**, you may continue using them as before by importing from Qiskit Algorithms.
+    However, be aware that Qiskit Algorithms is no longer officially supported and some of its
+    functionalities may not work in your use case. For any problems directly related to Qiskit
+    Algorithms, please open a GitHub issue at https://github
+    .com/qiskit-community/qiskit-algorithms. Should you want to include a Qiskit Algorithms
+    functionality that has not been incorporated in Qiskit Machine Learning, please open a
+    feature-request issue at https://github.com/qiskit-community/qiskit-machine-learning,
+    explaining why this change would be useful for you and other users.
 
   - |
     Four examples of upgrading the code can be found below.