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docs/tutorials/01_algorithms_introduction.ipynb

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docs/tutorials/03_vqe_simulation_with_noise.ipynb

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docs/tutorials/04_vqd.ipynb

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docs/tutorials/05_qaoa.ipynb

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docs/tutorials/06_grover.ipynb

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docs/tutorials/07_grover_examples.ipynb

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docs/tutorials/10_pvqd.ipynb

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docs/tutorials/11_VarQTE.ipynb

@@ -7,7 +7,7 @@
    },
    "source": [
     "# Variational Quantum Time Evolution\n",
-    "This notebook demonstrates how to use the Variational Quantum Time Evolution (VarQTE) algorithm for computing the time evolving state under a given Hamiltonian. Specifically, it introduces variational quantum imaginary and real time evolution based on McLachlan's variational principle, and shows how this can be leveraged using the `algorithms.time_evolvers.variational` sub-module."
+    "This notebook demonstrates how to use the Variational Quantum Time Evolution (VarQTE) algorithm for computing the time evolving state under a given Hamiltonian. Specifically, it introduces variational quantum imaginary and real time evolution based on McLachlan's variational principle, and shows how this can be leveraged using the `time_evolvers.variational` sub-module."
    ]
   },
   {
@@ -56,6 +56,7 @@
     "id": "caoBgyluhQ3L"
    },
    "source": [
+    "\n",
     "## Running VarQTE"
    ]
   },
@@ -96,9 +97,9 @@
     "id": "Y_9Jskw4m8nF"
    },
    "source": [
-    "# Imaginary Time Evolution\n",
+    "## Imaginary Time Evolution\n",
     "\n",
-    "Imaginary time evolution can be used, for example, to find the ground state or calculate the finite temperature expectation value of the system. Here, we will use the `VarQITE` class from `algorithms.time_evolvers.variational` to compute a ground state energy. Firstly, we need to choose an ansatz. We can use `EfficientSU2` to easily construct an ansatz, setting the number of repetitions using `reps`."
+    "Imaginary time evolution can be used, for example, to find the ground state or calculate the finite temperature expectation value of the system. Here, we will use the `VarQITE` class from `time_evolvers.variational` to compute a ground state energy. Firstly, we need to choose an ansatz. We can use `EfficientSU2` to easily construct an ansatz, setting the number of repetitions using `reps`."
    ]
   },
   {
@@ -235,7 +236,7 @@
     "id": "5oFggUXMktgG"
    },
    "source": [
-    "## Exact Classical Solution\n",
+    "### Exact Classical Solution\n",
     "In order to check whether our calculation using `VarQITE` is correct or not, we also call `SciPyImaginaryEvolver` to help us calculate the exact solution.\n",
     "\n",
     "Firstly, we can use `qiskit.quantum_info.Statevector` to help us get the statevector from the quantum circuit."
@@ -284,7 +285,7 @@
     "id": "afbae447lVAR"
    },
    "source": [
-    "## Results and Comparison\n",
+    "### Results and Comparison\n",
     "\n",
     "We use `evolution_result.observables` to get the variation over time of the expectation values of the Hamiltonian."
    ]
@@ -367,7 +368,7 @@
     "id": "zSGo5QcUlePi"
    },
    "source": [
-    "## Efficient classical calculation of gradients with VarQITE\n",
+    "### Efficient classical calculation of gradients with VarQITE\n",
     "\n",
     "You can use classically efficient gradient calculations to speed up the time evolution simulation by setting `qgt` to `ReverseQGT()` and `gradient` to `ReverseEstimatorGradient()`."
    ]
@@ -528,7 +529,7 @@
     "id": "m-tp9kdHmy55"
    },
    "source": [
-    "# Real Time Evolution\n",
+    "## Real Time Evolution\n",
     "Real time evolution is more suitable for tasks such as simulating quantum dynamics. For example, one can use `VarQRTE` to get time evolving expectation values of the magnetization.\n"
    ]
   },
@@ -538,7 +539,7 @@
     "id": "TPGhF7xfodnT"
    },
    "source": [
-    "## VarQRTE\n",
+    "### VarQRTE\n",
     "Again, the first step is to select an ansatz."
    ]
   },
@@ -784,7 +785,7 @@
     "id": "korN4ZZv8j_8"
    },
    "source": [
-    "## Efficient Way to run VarQRTE\n",
+    "### Efficient Way to run VarQRTE\n",
     "\n",
     "Similarly, we can set `qpt` as `ReverseQGT()` and `gradient` as `ReverseEstimatorGradient()` to speed up the calculation."
    ]
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  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

docs/tutorials/12_gradients_framework.ipynb

@@ -9,7 +9,7 @@
     }
    },
    "source": [
-    "# Qiskit Gradient Framework using Primitives\n",
+    "# Gradient Framework\n",
     "This tutorial demonstrates the use of the `qiskit_algorithms.gradients` module to evaluate quantum gradients using the [Qiskit Primitives](https://qiskit.org/documentation/apidoc/primitives.html).\n",
     "\n",
     "## Introduction\n",
@@ -723,4 +723,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}