[Docs] Tweaks to tutorial

- Add download links.
- Fix tutorial title.
- Fix table of contents.

Author: Yoric

examples/tutorial 1 - Using a Quantum Device to Extract Machine-Learning Features.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Tutorial 1: using a Quantum Device to extract machine-learning features\n",
     "\n",
+    "(download this tutorial [here](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb))\n",
+    "\n",
     "The companion library is a machine-learning tool that uses a quantum device to predict similarities and properties of graphs. In this tutorial, we will reproduce first part of the [QEK paper](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615) using the library's high-level API. The high-level goal of this tutorial is to predict toxicity properties of molecules, using quantum machine learning, but of course, the mechanisms involved are much more generic.\n",
     "\n",
     "By the end of this notebook, you will know how to:\n",
@@ -97,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Creating and executing a feature extractor from an emulator\n",
+    "## Creating and executing a feature extractor from an emulator\n",
     "\n",
     "The easiest way to process a graph is to compile and execute it for an emulator. QEK is built on top of Pulser, which provides several emulators. The simplest of these emulators is the `QutipEmulator`, which QEK uses for the `QutipExtractor`:"
    ]
@@ -162,7 +164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Creating and executing a feature extractor on a physical QPU\n",
+    "## Creating and executing a feature extractor on a physical QPU\n",
     "\n",
     "Once you have checked that low qubit sequences provide the results you expect on an emulator, you will generally want to move to a QPU.\n",
     "For this, you will need either physical access to a QPU, or an account with [PASQAL Cloud](https://docs.pasqal.cloud), which provides\n",
@@ -344,7 +346,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# What now?\n",
+    "## What now?\n",
     "\n",
     "What we have seen so far covers the use of a Quantum Device to extract machine-learning features.\n",
     "\n",

examples/tutorial 1a - Using a Quantum Device to Extract Machine-Learning Features - low-level.ipynb

@@ -4,7 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tutorial 1: using a Quantum Device to extract machine-learning features\n",
+    "# Tutorial 1 (low-level variant): using a Quantum Device to extract machine-learning features\n",
+    "\n",
+    "(download this tutorial [here](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201a%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features%20-%20low-level.ipynb))\n",
     "\n",
     "This notebook reproduces the first part of the [QEK paper](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615) using the library's low-level API.\n",
     "\n",
@@ -188,7 +190,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Executing the compiled graphs on an emulator\n",
+    "## Executing the compiled graphs on an emulator\n",
     "\n",
     "While our objective is to run the compiled graphs on a physical QPU, it is generally a good idea to test out some of these compiled graphs on an emulator first. For this example, we'll use the QutipEmulator, the simplest emulator provided with Pulser."
    ]
@@ -233,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Executing compiled graphs on a QPU\n",
+    "## Executing compiled graphs on a QPU\n",
     "\n",
     "Once you have checked that the compiled graphs work on an emulator, you will probably want to move to a QPU. Execution on a QPU takes\n",
     "resources polynomial in the number of qubits, which hopefully means an almost exponential speedup for large number of qubits.\n",
@@ -413,7 +415,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# What now?\n",
+    "## What now?\n",
     "\n",
     "What we have seen so far covers the use of a Quantum Device to extract machine-learning features.\n",
     "\n",

examples/tutorial 1b - Training SVM QEK - low-level - generic dataset.ipynb

@@ -4,7 +4,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Tutorial 1b: Training a SVM using QEK\n",
+    "## Tutorial 2 (variant): Training a SVM using QEK\n",
+    "\n",
+    "(download this tutorial [here](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201b%20-%20Training%20SVM%20QEK%20-%20low-level%20-%20generic%20dataset.ipynb))\n",
+    "\n",
     "\n",
     "This tutorial demonstrates how to create a classification model using the QEK kernel with a generic database. By the end of this guide, you will know how to:\n",
     "\n",

examples/tutorial 2 - Machine-Learning with the Quantum EvolutionKernel.ipynb

@@ -5,7 +5,9 @@
    "id": "80370a5d",
    "metadata": {},
    "source": [
-    "# Tutorial 2: Quantum Evolution Kernel-Based Machine Learning Tutorial"
+    "# Tutorial 2: Quantum Evolution Kernel-Based Machine Learning Tutorial\n",
+    "\n",
+    "(download this tutorial [here](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb))\n"
    ]
   },
   {

mkdocs.yml

@@ -46,6 +46,7 @@ theme:
 
 markdown_extensions:
 - admonition  # for notes
+- pymdownx.details # for collapsible
 - pymdownx.arithmatex: # for mathjax
     generic: true
 - pymdownx.highlight:
@@ -60,6 +61,7 @@ plugins:
 - mkdocs-jupyter:
     include: ["*.ipynb"]
     execute: True
+    include_source: True
     remove_tag_config:
         remove_input_tags:
               - ignore_warnings