[Doc] Fixing links - resolves #118 (#121)

Author: Yoric

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

@@ -16,9 +16,9 @@
     "2. Setup compilation and execution of these graphs for execution on a Quantum Device (either an emulator or a physical QPU).\n",
     "3. Launch the execution and extract the relevant machine-learning features.\n",
     "\n",
-    "A [companion notebook](./tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb) will guide you through machine-learning with QEK.\n",
+    "A [companion notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel/) ([source](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)) will guide you through machine-learning with QEK.\n",
     "\n",
-    "If, instead of using the library's high-level API, you prefer digging a bit closer to the qubits, you may prefer the companion [low-level notebook](./tutorial%201a%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features%20-%20low-level.ipynb) that mirrors this notebook, but using a lower-level API that will let you experiment with different quantum pulses."
+    "If, instead of using the library's high-level API, you prefer digging a bit closer to the qubits, you may prefer the companion [low-level notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%201a%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features%20-%20low-level/) ([source](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)) that mirrors this notebook, but using a lower-level API that will let you experiment with different quantum pulses."
    ]
   },
   {

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

@@ -17,9 +17,9 @@
     "3. Launch the execution of this compiled register/pulse on a quantum emulator or a physical QPU.\n",
     "4. Use the result to extract the relevant machine-learning features.\n",
     "\n",
-    "A [companion notebook](./tutorial%20220-20Machine20Learning20with20QEK.ipynb) reproduces the machine-learning part of the QEK paper.\n",
+    "A [companion notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel/) ([source](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)) will guide you through machine-learning with QEK.\n",
     "\n",
-    "If you are not interested in quantum-level details, you may prefer the companion [high-level notebook](./tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb) that mirrors this notebook, but using a higher-level API that takes care of all such issues."
+    "If you are not interested in quantum-level details, you may prefer the companion [high-level notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features/)([source](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb)) that mirrors this notebook, but using a higher-level API that takes care of all such issues."
    ]
   },
   {

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

@@ -19,10 +19,7 @@
     "4. **Extract QEK Features for Machine-Learning**: Utilize the results from the quantum execution to derive relevant features using the `QEK` Kernel.\n",
     "5. **Train a Machine Learning Model**: Build and train the model using the extracted features.\n",
     "\n",
-    "### Notes\n",
-    "\n",
-    "- A [companion notebook](./tutorial%20220-20Machine20Learning20with20QEK.ipynb) demonstrates advanced machine learning methods—including Grid Search—that can be used with the QEK kernel - using a real world molecular dataset.\n",
-    "- If you prefer to work at a higher level without getting into quantum-level details, you might opt for the [high-level notebook](./tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb), which abstracts these details using a more user-friendly API.\n",
+    "A [companion notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel/)([source](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel.ipynb)) demonstrates advanced machine learning methods—including Grid Search—that can be used with the QEK kernel - using a real world molecular dataset.\n",
     "\n",
     "---"
    ]

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

@@ -31,7 +31,7 @@
     "4. Integrating the kernel and SVM into a scikit-learn **Pipeline** for streamlined workflows.\n",
     "5. Performing hyperparameter optimization using **GridSearchCV** to improve model performance.\n",
     "\n",
-    "A [companion notebook](./tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb) will guide you through using a Quantum Device to extract machine-learning features from graphs.\n",
+    "A [companion notebook](https://pasqal-io.github.io/quantum-evolution-kernel/latest/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features/)([source](https://github.com/pasqal-io/quantum-evolution-kernel/blob/main/examples/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features.ipynb)) will guide you through using a Quantum Device to extract machine-learning features from graphs.\n",
     "\n",
     "\n",
     "In this tutorial, we use the results of the Quantum Device execution on a classical device (i.e. your computer) to create a Quantum Evolution Kernel. Since our algorithm combines steps that are executed on a Quantum Device and steps that are executed on a classical device, we call this a _hybrid algorithm_.\n",