Fix most of the aqua docs lint issues

docs/aqua/algorithms.rst

@@ -200,14 +200,15 @@ Additionally, VQE can be configured with the following parameters:
 Quantum Approximate Optimization Algorithm (QAOA)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-`QAOA <https://arxiv.org/abs/1411.4028>`__ is a well-known algorithm for finding approximate solutions to
-combinatorial-optimization problems.
-The QAOA implementation in Aqua directly uses `VQE <#variational-quantum-eigensolver-vqe>`__ for its general hybrid optimization structure.
+`QAOA <https://arxiv.org/abs/1411.4028>`__ is a well-known algorithm for finding approximate
+solutions to combinatorial-optimization problems.
+The QAOA implementation in Aqua directly uses `VQE <#variational-quantum-eigensolver-vqe>`__ for
+its general hybrid optimization structure.
 However, unlike VQE, which can be configured with arbitrary variational forms,
-QAOA uses its own fine-tuned variational form, which comprises :math:`p` parameterized global :math:`x` rotations and
-:math:`p` different parameterizations of the problem hamiltonian.
-As a result, unlike VQE, QAOA does not need to have a variational form specified as an input parameter,
-and is configured mainly by a single integer parameter, ``p``,
+QAOA uses its own fine-tuned variational form, which comprises :math:`p` parameterized global
+:math:`x` rotations and :math:`p` different parameterizations of the problem hamiltonian.
+As a result, unlike VQE, QAOA does not need to have a variational form specified as an input
+parameter, and is configured mainly by a single integer parameter, ``p``,
 which dictates the depth of the variational form, and thus affects the approximation quality.
 An initial state from Aqua's :ref:`initial-states` library may be supplied as well.
 
@@ -332,8 +333,8 @@ Quantum Phase Estimation (QPE)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 QPE (also sometimes abbreviated
-as PEA, for *Phase Estimation Algorithm*), takes two quantum registers, *control* and *target*, where the
-control consists of several qubits initially put in uniform
+as PEA, for *Phase Estimation Algorithm*), takes two quantum registers, *control* and *target*,
+where the control consists of several qubits initially put in uniform
 superposition, and the target a set of qubits prepared in an eigenstate
 (or, oftentimes, a guess of the eigenstate) of the unitary operator of
 a quantum system. QPE then evolves the target under the control using
@@ -368,7 +369,8 @@ configuration, QPE also exposes the following parameter settings:
 
        expansion_mode = "trotter" | "suzuki"
 
-   Two ``str`` values are permitted: ``"trotter"`` (Lloyd's method) or ``"suzuki"`` (for Trotter-Suzuki expansion),
+   Two ``str`` values are permitted: ``"trotter"`` (Lloyd's method) or ``"suzuki"``
+   (for Trotter-Suzuki expansion),
    with  ``"trotter"`` being the default one.
 
 -  The expansion order:
@@ -377,16 +379,17 @@ configuration, QPE also exposes the following parameter settings:
 
        expansion_order = 1 | 2 | ...
 
-   This parameter sets the Trotter-Suzuki expansion order.  A positive ``int`` value is expected.  The default value is ``2``.
+   This parameter sets the Trotter-Suzuki expansion order.  A positive ``int`` value is expected.
+   The default value is ``2``.
 
 -  The number of ancillae:
 
    .. code:: python
 
        num_ancillae = 1 | 2 | ...
 
-   This parameter sets the number of ancillary qubits to be used by QPE.  A positive ``int`` value is expected.
-   The default value is ``1``.
+   This parameter sets the number of ancillary qubits to be used by QPE.  A positive ``int``
+   value is expected. The default value is ``1``.
 
 .. topic:: Declarative Name
 
@@ -405,7 +408,8 @@ Iterative Quantum Phase Estimation (IQPE)
 
 IQPE, as its name
 suggests, iteratively computes the phase so as to require fewer qubits.
-It takes in the same set of parameters as `QPE <#quantum-phase-estimation-qpe>`__, except for the number of
+It takes in the same set of parameters as `QPE <#quantum-phase-estimation-qpe>`__, except
+for the number of
 ancillary qubits ``num_ancillae``, which is replaced by
 ``num_iterations`` (a positive ``int``, also defaulted to ``1``), and for the fact that an
 Inverse Quantum Fourier Transform (IQFT) is not used for IQPE.
@@ -435,9 +439,11 @@ applied to a particular operator :math:`A`.
 :math:`A` is assumed to operate on :math:`n + 1` qubits (plus possible ancillary qubits)
 where the :math:`n` qubits represent the uncertainty (in the form of a random distribution from the
 :ref:`random-distributions` library)
-and the last qubit, called the *objective qubit*, is used to represent the normalized objective value as its amplitude.
+and the last qubit, called the *objective qubit*, is used to represent the normalized objective
+value as its amplitude.
 In other words,
-:math:`A` is constructed such that the probability of measuring a '1' in the objective qubit is equal to the
+:math:`A` is constructed such that the probability of measuring a '1' in the objective qubit is
+equal to the
 value of interest.
 
 .. seealso::
@@ -465,7 +471,8 @@ expects the following inputs:
 
        a_factory
 
-   A ``CircuitFactory`` object that represents the uncertainty problem, i.e., the :math:`A` operator mentioned above.
+   A ``CircuitFactory`` object that represents the uncertainty problem, i.e., the :math:`A`
+   operator mentioned above.
 
 -  The optional problem unitary:
 
@@ -735,7 +742,8 @@ QSVM Variational can be configured with the following parameters:
 
    An integer value greater than or equal to ``3`` is expected.  The default is ``3``.
 
--  A Boolean indicating whether or not to print additional information when the algorithm is running:
+-  A Boolean indicating whether or not to print additional information when the algorithm is
+   running:
 
    .. code:: python
 
@@ -776,15 +784,18 @@ algorithms.
 Exact Eigensolver
 ^^^^^^^^^^^^^^^^^
 
-Exact Eigensolver computes up to the first :math:`k` eigenvalues of a complex square matrix of dimension
+Exact Eigensolver computes up to the first :math:`k` eigenvalues of a complex square matrix of
+dimension
 :math:`n \times n`, with :math:`k \leq n`.
-It can be configured with an ``int`` parameter ``k`` indicating the number of eigenvalues to compute:
+It can be configured with an ``int`` parameter ``k`` indicating the number of eigenvalues to
+compute:
 
 .. code:: python
 
     k = 1 | 2 | ... | n
 
-Specifically, the value of this parameter must be an ``int`` value ``k`` in the range :math:`[1,n]`. The default is ``1``.
+Specifically, the value of this parameter must be an ``int`` value ``k`` in the range
+:math:`[1,n]`. The default is ``1``.
 
 .. topic:: Declarative Name
 
@@ -824,11 +835,14 @@ CPLEX Ising can be configured with the following parameters:
 
        thread = 0 | 1 | 2 | ...
 
-   A non-negative ``int`` value is expected. Setting ``thread`` to ``0`` lets CPLEX decide the number of threads to allocate, but this may
+   A non-negative ``int`` value is expected. Setting ``thread`` to ``0`` lets CPLEX decide the
+   number of threads to allocate, but this may
    not be ideal for small problems.  Any value
-   greater than ``0`` specifically sets the thread count.  The default value is ``1``, which is ideal for small problems.
+   greater than ``0`` specifically sets the thread count.  The default value is ``1``, which is
+   ideal for small problems.
 
--  Decides what CPLEX reports to the screen and records in a log during mixed integer optimization (MIP).
+-  Decides what CPLEX reports to the screen and records in a log during mixed integer
+   optimization (MIP).
 
    .. code:: python
 
@@ -848,6 +862,7 @@ CPLEX Ising can be configured with the following parameters:
    In Aqua, CPLEX supports the ``ising`` problem.
 
 .. _avm-rbf-kernel:
+
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 Support Vector Machine Radial Basis Function Kernel (SVM Classical)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/aqua/aqua.rst

@@ -12,7 +12,8 @@ created :ref:`aqua-library` that can be invoked directly or via domain-specific
 applications:
 :ref:`aqua-chemistry`, :ref:`aqua-ai`, :ref:`aqua-optimization` and
 :ref:`aqua-finance`.
-Finally, :ref:`aqua-tutorials` is a companion library of notebooks, input files and sample code made available in the form of a
+Finally, :ref:`aqua-tutorials` is a companion library of notebooks, input files
+and sample code made available in the form of a
 `GitHub repository <https://github.com/Qiskit/aqua-tutorials>`__.
 
 This part of the documentation will first cover Aqua as a library of quantum applications,