Migrate tutorials from qiskit-tutorials

Author: ElePT

docs/tutorials/01_algorithms_introduction.ipynb

@@ -61,7 +61,7 @@
     }
    ],
    "source": [
-    "ansatz.decompose().draw(\"mpl\", style=\"iqx\")"
+    "ansatz.decompose().draw('mpl', style='iqx')"
    ]
   },
   {
@@ -79,7 +79,7 @@
     "\n",
     "Algorithms rely on the primitives to evaluate expectation values or sample circuits. The primitives can be based on a simulator or real device and can be used interchangeably in the algorithms, as they all implement the same interface.\n",
     "\n",
-    "In the VQE, we have to evaluate expectation values, so for example we can use the `qiskit.primitives.Estimator` which is shipped with the default Qiskit Terra installation."
+    "In the VQE, we have to evaluate expectation values, so for example we can use the [qiskit.primitives.Estimator](https://qiskit.org/documentation/stubs/qiskit.primitives.Estimator.html) which is shipped with the default Qiskit Terra installation."
    ]
   },
   {
@@ -97,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This estimator uses an exact, statevector simulation to evaluate the expectation values. We can also use a shot-based and noisy simulators or real backends instead. For more information of the simulators you can check out [Qiskit Aer](https://qiskit.org/documentation/apidoc/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/).\n",
+    "This estimator uses an exact, statevector simulation to evaluate the expectation values. We can also use a shot-based and noisy simulators or real backends instead. For more information of the simulators you can check out [Qiskit Aer](https://qiskit.org/ecosystem/aer/apidocs/aer_primitives.html) and for the actual hardware [Qiskit IBM Runtime](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/).\n",
     "\n",
     "With all the ingredients ready, we can now instantiate the VQE:"
    ]
@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can call the [compute_mininum_eigenvalue()](https://qiskit.org/documentation/stubs/qiskit_algorithms.minimum_eigensolvers.VQE.compute_minimum_eigenvalue.html#qiskit_algorithms.minimum_eigensolvers.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.org/documentation/stubs/qiskit.algorithms.minimum_eigensolvers.VQE.compute_minimum_eigenvalue.html#qiskit.algorithms.minimum_eigensolvers.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."
    ]
   },
   {
@@ -205,9 +205,9 @@
     "\n",
     "To close off let's also change the estimator primitive inside the a VQE. Maybe you're satisfied with the simulation results and now want to use a shot-based simulator, or run on hardware!\n",
     "\n",
-    "In this example we're changing to a shot-based estimator, still using Qiskit Terra's reference primitive. However you could replace the primitive by e.g. Qiskit Aer's estimator (`qiskit_aer.primitives.Estimator`) or even a real backend (`qiskit_ibm_runtime.Estimator`).\n",
+    "In this example we're changing to a shot-based estimator, still using Qiskit Terra's reference primitive. However you could replace the primitive by e.g. Qiskit Aer's estimator ([qiskit_aer.primitives.Estimator](https://qiskit.org/ecosystem/aer/stubs/qiskit_aer.primitives.Estimator.html#qiskit_aer.primitives.Estimator)) or even a real backend ([qiskit_ibm_runtime.Estimator](https://qiskit.org/documentation/partners/qiskit_ibm_runtime/stubs/qiskit_ibm_runtime.Estimator.html#qiskit_ibm_runtime.Estimator)).\n",
     "\n",
-    "For noisy loss functions, the SPSA optimizer typically performs well, so we also update the optimizer. See also the [noisy VQE tutorial](https://qiskit.org/documentation/tutorials/algorithms/03_vqe_simulation_with_noise.html) for more details on shot-based and noisy simulations."
+    "For noisy loss functions, the SPSA optimizer typically performs well, so we also update the optimizer. See also the [noisy VQE tutorial](https://qiskit.org/documentation/tutorials/algorithms/03_vqe_simulation_with_noise.html) for more details on shot-based and noisy simulations. "
    ]
   },
   {
@@ -335,4 +335,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}