[Doc] Reorganizing doc, again (#85)

Author: Yoric

docs/index.md

@@ -18,4 +18,4 @@ Users setting their first steps into quantum computing will learn how to impleme
 
 The GitHub repository is open for contributions!
 
-Don't forget to read the [Contributor License Agreement](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/docs/index.md).
+Don't forget to read the [Contributor License Agreement](CONTRIBUTOR AGREEMENT.md).

docs/tutorials.md

@@ -1,10 +1,13 @@
-# Quantum Extraction
+# High-Level tutorials
 
-These tutorials will teach you how to use the quantum APIs for feature-extraction:
+These tutorials will guide you, step-to-step, from loading graphs to machine learning.
 
 1. [Using a Quantum Device to extract machine-learning features (high-level)](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb);
-2. [Using a Quantum Device to extract machine-learning features (low-level)](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);
+2. [Machine Learning with the Quantum Evolution Kernel](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)
 
-# Machine Learning
 
-1. [Machine Learning with the Quantum Evolution Kernel](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)
+# Going lower-level
+
+At some point, you may decide that you need more control, or wish to understand better how the pipeline works. These tutorials will guide you through the same features using lower-level APIs.
+
+1. [Using a Quantum Device to extract machine-learning features (low-level)](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);

mkdocs.yml

@@ -4,12 +4,14 @@ repo_name: "Quantum Evolution Kernel"
 
 nav:
   - Overview: index.md
-  - Installation: installation.md
-  - Usage: usage.md
-  - Tutorials: tutorials.md
+  - Getting started:
+    - Installation: installation.md
+    - Usage: usage.md
+    - Tutorials: tutorials.md
   - Reference: $api/qek.***
-  - License: LICENSE.md
-  - CLA: CONTRIBUTOR AGREEMENT.md
+  - Open-source:
+    - Licence (MIT-Derived): LICENSE.md
+    - Contributor Agreement: CONTRIBUTOR AGREEMENT.md
 
 theme:
   name: material

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "hatchling.build"
 name = "quantum-evolution-kernel"
 description = "A Python library designed for the machine learning community to help users design quantum-driven similarity metrics for graphs and to use them inside kernel-based machine learning algorithms for graph data.ide the right environment to explore new ideas - both in terms of methodologies and data domain - while always interacting with a simple and intuitive QPU interface."
 readme = "README.md"
-version = "0.2.8"
+version = "0.2.9"
 requires-python = ">=3.10,<3.13"
 license = { text = "MIT-derived" }
 keywords = ["quantum"]