Remove qiskit.org links and docs deploy (#604)

CONTRIBUTING.md

@@ -5,7 +5,7 @@
 First please read the overall project contributing guidelines. These are
 included in the Qiskit documentation here:
 
-<https://qiskit.org/documentation/contributing_to_qiskit.html>
+https://github.com/Qiskit/qiskit/blob/main/CONTRIBUTING.md
 
 ## Contributing to Qiskit Optimization
 
@@ -64,8 +64,7 @@ please ensure that:
    to see the changes formatted output is as expected. You will find an index.html
    file in docs\_build\html and you can navigate from there.
 
-   Please note that a spell check is run in CI, on the docstrings, since the text
-   becomes part of the online [API Documentation](https://qiskit.org/documentation/).
+   Please note that a spell check is run in CI on the docstrings.
 
    You can run `make spell` locally to check spelling though you would need to
    [install pyenchant](https://pyenchant.github.io/pyenchant/install.html) and be using

docs/getting_started.rst

@@ -7,9 +7,9 @@ Getting started
 Installation
 ============
 
-Qiskit Optimization depends Qiskit. which has its own
-`Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing the
-installation options and its supported environments/platforms. You should refer to
+Qiskit Optimization depends on Qiskit, which has its own
+`installation instructions <https://docs.quantum.ibm.com/start/install>`__ detailing the
+installation options for Qiskit and its supported environments/platforms. You should refer to
 that first. Then the information here can be followed which focuses on the additional installation
 specific to Qiskit Optimization.
 
@@ -21,13 +21,9 @@ See :ref:`optional_installs` for more information.
 
     .. tab-item:: Start locally
 
-        The simplest way to get started is to first follow the `getting started 'Start locally' guide for
-        Qiskit <https://qiskit.org/documentation/getting_started.html>`__
+        The simplest way to get started is to follow the `Qiskit installation instructions <https://docs.quantum.ibm.com/start/install>`__
 
-        In your virtual environment where you installed Qiskit simply add ``optimization`` to the
-        extra list in a similar manner to how the extra ``visualization`` support is installed for
-        Qiskit, i.e:
-        In your virtual environment, where you installed Qiskit, install Qiskit Optimization as follows:
+        In your virtual environment where you installed Qiskit, also install ``qiskit-optimization``:
 
         .. code:: sh
 
@@ -48,7 +44,7 @@ See :ref:`optional_installs` for more information.
 
        Since Qiskit Optimization depends on Qiskit, and its latest changes may require new or changed
        features of Qiskit, you should first follow Qiskit's `"Install from source"` instructions
-       here `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__
+       `here <https://docs.quantum.ibm.com/start/install-qiskit-source>`__
 
        .. raw:: html
 

docs/migration/01_migration_guide_to_v0.5.rst

@@ -9,7 +9,7 @@ Overview
 
 Qiskit Terra v0.22 introduces new algorithm implementations that
 leverage `Qiskit
-Primitives <https://qiskit.org/documentation/apidoc/primitives.html>`__
+Primitives <https://docs.quantum.ibm.com/api/qiskit/primitives>`__
 (Estimator and Sampler). The former algorithm implementations that
 leverage opflow will be deprecated in the future release.
 
@@ -19,7 +19,7 @@ of Qiskit Terra v0.22 until the former algorithms are deprecated.
 It is not the intention to provide detailed explanations of the
 primitives in this migration guide. We suggest that you read the
 `corresponding
-resources <https://qiskit.org/documentation/apidoc/primitives.html>`__
+resources <https://docs.quantum.ibm.com/api/qiskit/primitives>`__
 of the Qiskit Terra documentation instead.
 
 We use ``qiskit.primitives.Sampler`` in this guide as an example of

docs/tutorials/12_quantum_random_access_optimizer.ipynb

@@ -51,7 +51,7 @@
     "To begin, we utilize the `Maxcut` class from Qiskit Optimization's application module. It allows us to generate a `QuadraticProgram` representation of the given graph.\n",
     "\n",
     "Note that once our problem has been represented as a `QuadraticProgram`, it will need to be converted to the correct type, a [quadratic unconstrained binary optimization (QUBO)](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization) problem, so that it is compatible with QRAO.\n",
-    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit.org/documentation/optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
+    "A `QuadraticProgram` generated by `Maxcut` is already a QUBO, but if you define your own problem be sure you convert it to a QUBO before proceeding. Here is [a tutorial](https://qiskit-community.github.io/qiskit-optimization/tutorials/02_converters_for_quadratic_programs.html) on converting `QuadraticPrograms`."
    ]
   },
   {
@@ -113,7 +113,7 @@
     "\n",
     "Once we have appropriately configured our problem, we proceed to encode it using the `QuantumRandomAccessEncoding` class from the `qrao` module. This encoding step allows us to generate a quantum Hamiltonian operator that represents our problem. In particular, we employ a Quantum Random Access Code (QRAC) to encode multiple classical binary variables (corresponding to the nodes of our max-cut graph) into each qubit.\n",
     "\n",
-    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit.org/documentation/optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
+    "It's important to note that the resulting \"relaxed\" Hamiltonian, produced by this encoding, will not be diagonal. This differs from the standard workflow in `qiskit-optimization`, which typically generates a diagonal (Ising) Hamiltonian suitable for optimization using a `MinimumEigenOptimizer`. You can find a tutorial on the `MinimumEigenOptimizer` [here](https://qiskit-community.github.io/qiskit-optimization/tutorials/03_minimum_eigen_optimizer.html).\n",
     "\n",
     "In our encoding process, we employ a $(3,1,p)-$QRAC, where each qubit can accommodate a maximum of 3 classical binary variables. The parameter $p$ represents the bit recovery probability achieved through measurement. Depending on the nature of the problem, some qubits may have fewer than 3 classical variables assigned to them. To evaluate the compression achieved, we can examine the `compression_ratio` attribute of the encoding, which provides the ratio between the number of original binary variables and the number of qubits used (at best, a factor of 3)."
    ]
@@ -552,7 +552,7 @@
     "\n",
     "By invoking `qrao.solve_relaxed()`, we obtain two essential outputs:\n",
     "\n",
-    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://qiskit.org/documentation/stubs/qiskit.algorithms.MinimumEigensolverResult.html) for a comprehensive explanation of the entries within this object.\n",
+    "- `MinimumEigensolverResult`: This object contains the results of running the minimum eigen optimizer such as the VQE on the relaxed problem. It provides information about the eigenvalue, and other relevant details. You can refer to the Qiskit Algorithms [documentation](https://docs.quantum.ibm.com/api/qiskit/qiskit.algorithms.MinimumEigensolverResult) for a comprehensive explanation of the entries within this object.\n",
     "- `RoundingContext`: This object encapsulates essential information about the encoding and the solution of the relaxed problem in a form that is ready for consumption by the rounding schemes."
    ]
   },

setup.py

@@ -50,7 +50,7 @@
     long_description_content_type="text/markdown",
     url="https://github.com/qiskit-community/qiskit-optimization",
     author="Qiskit Optimization Development Team",
-    author_email="hello@qiskit.org",
+    author_email="qiskit@us.ibm.com",
     license="Apache-2.0",
     classifiers=[
         "Environment :: Console",