Update intersphinx links and others (#598)

* Update intersphinx links and others

* Try to avoid url being spell checked

README.md

@@ -13,7 +13,7 @@ A uniform interface as well as automatic conversion between different problem re
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the
 GroverOptimizer, leveraging fundamental algorithms provided by
-[Qiskit Algorithms](https://qiskit.org/ecosystem/algorithms/). Furthermore, the modular design
+[Qiskit Algorithms](https://qiskit-community.github.io/qiskit-algorithms/). Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,
 validation, and benchmarking.
@@ -32,7 +32,7 @@ pip install qiskit-optimization
 If you want to work on the very latest work-in-progress versions, either to try features ahead of
 their official release or if you want to contribute to Optimization, then you can install from source.
 To do this follow the instructions in the
- [documentation](https://qiskit.org/ecosystem/optimization/getting_started.html#installation).
+ [documentation](https://qiskit-community.github.io/qiskit-optimization/getting_started.html#installation).
 
 
 ----------------------------------------------------------------------------------------------------
@@ -108,7 +108,7 @@ print(result.prettyprint())  # prints solution, x=[1, 0, 1, 0], the cost, fval=4
 ### Further examples
 
 Learning path notebooks may be found in the
-[optimization tutorials](https://qiskit.org/ecosystem/optimization/tutorials/index.html) section
+[optimization tutorials](https://qiskit-community.github.io/qiskit-optimization/tutorials/index.html) section
 of the documentation and are a great place to start.
 
 ----------------------------------------------------------------------------------------------------

docs/conf.py

@@ -183,8 +183,8 @@
     "scipy": ("https://docs.scipy.org/doc/scipy", None),
     "networkx": ("https://networkx.org/documentation/stable", None),
     "docplex.mp": ("https://ibmdecisionoptimization.github.io/docplex-doc/mp", None),
-    "qiskit": ("https://qiskit.org/documentation/", None),
-    "qiskit_algorithms": ("https://qiskit.org/ecosystem/algorithms", None),
+    "qiskit": ("https://docs.quantum.ibm.com/api/qiskit", None),
+    "qiskit_algorithms": ("https://qiskit-community.github.io/qiskit-algorithms", None),
 }
 
 html_context = {"analytics_enabled": True}

docs/index.rst

@@ -16,7 +16,7 @@ A uniform interface as well as automatic conversion between different problem re
 allows users to solve problems using a large set of algorithms, from variational quantum algorithms,
 such as the Quantum Approximate Optimization Algorithm QAOA, to Grover Adaptive Search using the
 GroverOptimizer, leveraging fundamental algorithms provided by
-`Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`__. Furthermore, the modular design
+`Qiskit Algorithms <https://qiskit-community.github.io/qiskit-algorithms/>`__. Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,
 validation, and benchmarking.

docs/migration/02_migration_guide_to_v0.6.rst

@@ -16,7 +16,7 @@ Qiskit Optimization v0.6 supports only the new algorithms of Qiskit Algorithms.
 It is not the intention to provide detailed explanations of the
 new Qiskit Algorithms in this migration guide. We suggest that you read the
 `corresponding
-resources <https://qiskit.org/ecosystem/algorithms/index.html>`__
+resources <https://qiskit-community.github.io/qiskit-algorithms/index.html>`__
 of the Qiskit Algorithms documentation instead.
 
 We can basically use the existing codes by replacing ``qiskit.algorithms``

docs/tutorials/08_cvar_optimization.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "## Introduction\n",
     "\n",
-    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by [Qiskit Algorithms](https://qiskit.org/ecosystem/algorithms/). Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
+    "This notebook shows how to use the Conditional Value at Risk (CVaR) objective function introduced in [1] within the variational quantum optimization algorithms provided by [Qiskit Algorithms](https://qiskit-community.github.io/qiskit-algorithms/). Particularly, it is shown how to setup the `MinimumEigenOptimizer` using `SamplingVQE` accordingly. \n",
     "For a given set of shots with corresponding objective values of the considered optimization problem, the CVaR with confidence level $\\alpha \\in [0, 1]$ is defined as the average of the $\\alpha$ best shots.\n",
     "Thus, $\\alpha = 1$ corresponds to the standard expected value, while $\\alpha=0$ corresponds to the minimum of the given shots, and $\\alpha \\in (0, 1)$ is a tradeoff between focusing on better shots, but still applying some averaging to smoothen the optimization landscape.\n",
     "\n",

docs/tutorials/12_quantum_random_access_optimizer.ipynb

@@ -303,7 +303,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [MinimumEigensolverResult](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.MinimumEigensolverResult.html) that results from performing VQE on the relaxed Hamiltonian is available:"
+    "The [MinimumEigensolverResult](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.MinimumEigensolverResult.html) that results from performing VQE on the relaxed Hamiltonian is available:"
    ]
   },
   {

qiskit_optimization/__init__.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2019, 2023.
+# (C) Copyright IBM 2019, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -29,9 +29,10 @@
 (:class:`~qiskit_algorithms.QAOA`), to
 `Grover Adaptive Search <https://arxiv.org/abs/quant-ph/9607014>`_
 (:class:`~algorithms.GroverOptimizer`), leveraging
-fundamental `minimum eigensolvers <https://qiskit.org/ecosystem/algorithms/apidocs/
-qiskit_algorithms.html#minimum-eigensolvers>`_ provided by
-`Qiskit Algorithms <https://qiskit.org/ecosystem/algorithms/>`_.
+fundamental `minimum eigensolvers
+<https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.html#minimum-eigensolvers>`_
+provided by
+`Qiskit Algorithms <https://qiskit-community.github.io/qiskit-algorithms/>`_.
 Furthermore, the modular design
 of the optimization module allows it to be easily extended and facilitates rapid development and
 testing of new algorithms. Compatible classical optimizers are also provided for testing,

setup.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2021, 2023.
+# (C) Copyright IBM 2021, 2024.
 #
 # This code is licensed under the Apache License, Version 2.0. You may
 # obtain a copy of this license in the LICENSE.txt file in the root directory
@@ -80,7 +80,7 @@
     },
     project_urls={
         "Bug Tracker": "https://github.com/qiskit-community/qiskit-optimization/issues",
-        "Documentation": "https://qiskit.org/ecosystem/optimization/",
+        "Documentation": "https://qiskit-community.github.io/qiskit-optimization/",
         "Source Code": "https://github.com/qiskit-community/qiskit-optimization",
     },
     zip_safe=False,