Update intersphinx links and others (#142)

(cherry picked from commit 068f3ed)

docs/conf.py

@@ -1,6 +1,6 @@
 # This code is part of a Qiskit project.
 #
-# (C) Copyright IBM 2023.
+# (C) Copyright IBM 2023, 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
@@ -120,7 +120,7 @@
     "python": ("https://docs.python.org/3", None),
     "numpy": ("https://numpy.org/doc/stable", None),
     "scipy": ("https://docs.scipy.org/doc/scipy", None),
-    "qiskit": ("https://qiskit.org/documentation/", None),
+    "qiskit": ("https://docs.quantum.ibm.com/api/qiskit/", None),
 }
 
 # -- Options for HTML output -------------------------------------------------

docs/tutorials/01_algorithms_introduction.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "## How is the algorithm library structured?\n",
     "\n",
-    "`qiskit_algorithms` provides a number of [algorithms](https://qiskit.org/ecosystem/algorithms/apidocs/algorithms.html) grouped by category, according to the task they can perform. For instance `Minimum Eigensolvers` to find the smallest eigen value of an operator, for example ground state energy of a chemistry Hamiltonian or a solution to an optimization problem when expressed as an Ising Hamiltonian. There are `Time Evolvers` for the time evolution of quantum systems and `Amplitude Estimators` for value estimation that can be used say in financial applications. The full set of categories can be seen in the documentation link above.\n",
+    "`qiskit_algorithms` provides a number of [algorithms](https://qiskit-community.github.io/qiskit-algorithms/apidocs/algorithms.html) grouped by category, according to the task they can perform. For instance `Minimum Eigensolvers` to find the smallest eigen value of an operator, for example ground state energy of a chemistry Hamiltonian or a solution to an optimization problem when expressed as an Ising Hamiltonian. There are `Time Evolvers` for the time evolution of quantum systems and `Amplitude Estimators` for value estimation that can be used say in financial applications. The full set of categories can be seen in the documentation link above.\n",
     "\n",
     "Algorithms are configurable, and part of the configuration will often be in the form of smaller building blocks. For instance `VQE`, the Variational Quantum Eigensolver, it takes a trial wavefunction, in the form of a `QuantumCircuit` and a classical optimizer among other things.\n",
     "\n",
@@ -121,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can call the [compute_mininum_eigenvalue()](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VQE.html#qiskit_algorithms.VQE.compute_minimum_eigenvalue) method. The latter is the interface of choice for the application modules, such as Nature and Optimization, in order that they can work interchangeably with any algorithm within the specific category."
+    "Now we can call the [compute_mininum_eigenvalue()](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VQE.html#qiskit_algorithms.VQE.compute_minimum_eigenvalue) method. The latter is the interface of choice for the application modules, such as Nature and Optimization, in order that they can work interchangeably with any algorithm within the specific category."
    ]
   },
   {
@@ -337,4 +337,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/tutorials/02_vqe_advanced_options.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Advanced VQE Options\n",
     "\n",
-    "In the first algorithms tutorial, you learned how to set up a basic [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VQE.html) algorithm. Now, you will see how to provide more advanced configuration parameters to explore the full range of the variational algorithms provided in this library: [VQE](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VQE.html), [QAOA](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.QAOA.html) and [VQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VQD.html) among others. In particular, this tutorial will cover how to set up a `callback` to monitor convergence and the use of custom `initial point`s and `gradient`s."
+    "In the first algorithms tutorial, you learned how to set up a basic [VQE](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VQE.html) algorithm. Now, you will see how to provide more advanced configuration parameters to explore the full range of the variational algorithms provided in this library: [VQE](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VQE.html), [QAOA](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.QAOA.html) and [VQD](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VQD.html) among others. In particular, this tutorial will cover how to set up a `callback` to monitor convergence and the use of custom `initial point`s and `gradient`s."
    ]
   },
   {
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First, you need a qubit operator for VQE. For this example, you can use the same operator as used in the algorithms introduction, which was originally computed by [Qiskit Nature](https://qiskit.org/ecosystem/nature/) for an H2 molecule."
+    "First, you need a qubit operator for VQE. For this example, you can use the same operator as used in the algorithms introduction, which was originally computed by [Qiskit Nature](https://qiskit-community.github.io/qiskit-nature/) for an H2 molecule."
    ]
   },
   {
@@ -177,7 +177,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, since the above problem is still easily tractable classically, you can use [NumPyMinimumEigensolver](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.NumPyMinimumEigensolver.html) to compute a reference value for the solution."
+    "Finally, since the above problem is still easily tractable classically, you can use [NumPyMinimumEigensolver](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.NumPyMinimumEigensolver.html) to compute a reference value for the solution."
    ]
   },
   {
@@ -246,7 +246,7 @@
    "source": [
     "## Gradients\n",
     "\n",
-    "In the variational algorithms, if the provided optimizer uses a gradient-based technique, the default gradient method will be finite differences. However, these classes include an option to pass custom gradients via the `gradient` parameter, which can be any of the provided methods within the [gradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.gradients.html) framework, which fully supports the use of primitives. This section shows how to use custom gradients in the VQE workflow.\n",
+    "In the variational algorithms, if the provided optimizer uses a gradient-based technique, the default gradient method will be finite differences. However, these classes include an option to pass custom gradients via the `gradient` parameter, which can be any of the provided methods within the [gradient](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.gradients.html) framework, which fully supports the use of primitives. This section shows how to use custom gradients in the VQE workflow.\n",
     "\n",
     "The first step is to initialize both the corresponding primitive and primitive gradient:"
    ]
@@ -267,7 +267,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, you can inspect an SLSQP run using the [FiniteDiffEstimatorGradient](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.gradients.FiniteDiffEstimatorGradient.html) from above:"
+    "Now, you can inspect an SLSQP run using the [FiniteDiffEstimatorGradient](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.gradients.FiniteDiffEstimatorGradient.html) from above:"
    ]
   },
   {
@@ -532,4 +532,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/tutorials/04_vqd.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Variational Quantum Deflation (VQD) Algorithm\n",
     "\n",
-    "This notebook demonstrates how to use our implementation of the [Variational Quantum Deflation (VQD)](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VQD.html) algorithm for computing higher energy states of a Hamiltonian, as introduced in this [reference paper](https://arxiv.org/abs/1805.08138)."
+    "This notebook demonstrates how to use our implementation of the [Variational Quantum Deflation (VQD)](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VQD.html) algorithm for computing higher energy states of a Hamiltonian, as introduced in this [reference paper](https://arxiv.org/abs/1805.08138)."
    ]
   },
   {
@@ -365,4 +365,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

docs/tutorials/05_qaoa.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Quantum Approximate Optimization Algorithm\n",
     "\n",
-    "This notebook demonstrates the implementation of the Quantum Approximate Optimization Algorithm ([QAOA](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.QAOA.html)) for a graph partitioning problem (finding the maximum cut), and compares it to a solution using the brute-force approach."
+    "This notebook demonstrates the implementation of the Quantum Approximate Optimization Algorithm ([QAOA](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.QAOA.html)) for a graph partitioning problem (finding the maximum cut), and compares it to a solution using the brute-force approach."
    ]
   },
   {
@@ -389,4 +389,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/tutorials/07_grover_examples.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Grover's algorithm examples\n",
     "\n",
-    "This notebook has examples demonstrating how to use the Qiskit Algorithms [Grover](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.Grover.html) search algorithm, with different oracles."
+    "This notebook has examples demonstrating how to use the Qiskit Algorithms [Grover](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.Grover.html) search algorithm, with different oracles."
    ]
   },
   {
@@ -269,4 +269,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

docs/tutorials/10_pvqd.ipynb

@@ -16,7 +16,7 @@
     "\n",
     "where $e^{-i\\Delta_t H}$ is calculated with a Trotter expansion (using e.g. the [PauliEvolutionGate](https://qiskit.org/documentation/stubs/qiskit.circuit.library.PauliEvolutionGate.html) in Qiskit!).\n",
     "\n",
-    "The following tutorial explores the p-VQD algorithm, which is available as the [PVQD](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.PVQD.html) class. For details on the algorithm, see the original paper: [Barison et al. Quantum 5, 512 (2021)](https://quantum-journal.org/papers/q-2021-07-28-512/#)."
+    "The following tutorial explores the p-VQD algorithm, which is available as the [PVQD](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.PVQD.html) class. For details on the algorithm, see the original paper: [Barison et al. Quantum 5, 512 (2021)](https://quantum-journal.org/papers/q-2021-07-28-512/#)."
    ]
   },
   {

docs/tutorials/11_VarQTE.ipynb

@@ -66,7 +66,7 @@
     "id": "WG-18slyLnVM"
    },
    "source": [
-    "In this tutorial, we will use two classes that extend `VarQTE`, `VarQITE` ([Variational Quantum Imaginary Time Evolution](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VarQITE.html#qiskit_algorithms.VarQITE)) and `VarQRTE` ([Variational Quantum Real Time Evolution](https://qiskit.org/ecosystem/algorithms/stubs/qiskit_algorithms.VarQRTE.html)) for time evolution.\n",
+    "In this tutorial, we will use two classes that extend `VarQTE`, `VarQITE` ([Variational Quantum Imaginary Time Evolution](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VarQITE.html#qiskit_algorithms.VarQITE)) and `VarQRTE` ([Variational Quantum Real Time Evolution](https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.VarQRTE.html)) for time evolution.\n",
     "We can use a simple Ising model on a spin chain to illustrate this. Let us consider the following Hamiltonian:\n",
     "\n",
     "$$H = -J\\sum_{i=0}^{L-2}Z_{i}Z_{i+1} - h\\sum_{i=0}^{L-1}X_i$$\n",
@@ -955,4 +955,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}