fix docs (#296)

Author: MoritzWillmann

.github/workflows/doc_checks.yml

@@ -16,5 +16,5 @@ jobs:
         - uses: sQUlearn/sphinx-action@master
           with:
             docs-folder: "docs/"
-            pre-build-command: "pip3 install ."
+            pre-build-command: "pip3 install .[docs]"
             build-command: "sphinx-build -b html . _build -W --keep-going"

.github/workflows/publish_docs.yml

@@ -24,7 +24,7 @@ jobs:
             uses: sQUlearn/sphinx-action@master
             with:
               docs-folder: "docs/"
-              pre-build-command: "pip3 install ."
+              pre-build-command: "pip3 install .[docs]"
               build-command: "sphinx-build -b html . _build -W"
           - name: Commit Files
             run: |

docs/requirements.txt

@@ -8,7 +8,7 @@
     "\n",
     "### Introduction\n",
     "\n",
-    "This tutorial demonstrates the setup and implementation of error mitigation techniques for Qiskit's primitives within the context of Quantum Neural Networks (QNNs) in sQUlearn. Firstly, we will train a small QNN using a noise-free and error-free simulator to fit a parabolic dataset. Subsequently, we will illustrate how to configure the `Executor` class to establish a connection with IBM Quantum devices. We assume that you have already set up an IBM account and have access to IBM Quantum devices. For detailed instructions on registering and configuring your IBM Quantum account, please refer to the following link: [IBM Quantum Account Management](https://qiskit.org/ecosystem/ibm-runtime/how_to/account-management.html).\n",
+    "This tutorial demonstrates the setup and implementation of error mitigation techniques for Qiskit's primitives within the context of Quantum Neural Networks (QNNs) in sQUlearn. Firstly, we will train a small QNN using a noise-free and error-free simulator to fit a parabolic data set. Subsequently, we will illustrate how to configure the `Executor` class to establish a connection with IBM Quantum devices. We assume that you have already set up an IBM account and have access to IBM Quantum devices. For detailed instructions on registering and configuring your IBM Quantum account, please refer to the following link: [IBM Quantum Account Management](https://qiskit.org/ecosystem/ibm-runtime/how_to/account-management.html).\n",
     "\n",
     "Given the potentially lengthy queuing times for the free quantum devices, we will replicate the noise model of a quantum device and use the QASM simulator to emulate the real characteristics of a quantum computer. The simulator employed in this task, known as the `ibmq_qasm_simulator`, operates within the IBM Quantum cloud and can be accessed similarly to the actual quantum devices.\n",
     "\n",

examples/tutorials/qnn_backend_mitigation.ipynb

@@ -45,17 +45,9 @@ dynamic = ["version", "description"]
 dev = [
     "black",
     "flit",
-    "myst-parser",
     "nbmake",
-    "nbsphinx",
-    "nbsphinx_link",
     "pylint",
     "pytest",
-    "sphinx",
-    "sphinxcontrib-spelling",
-    "sphinx-rtd-theme",
-    "sphinx-sitemap",
-    "jupyter-sphinx",
 ]
 examples = [
     "jupyter",
@@ -66,6 +58,19 @@ examples = [
     "seaborn",
     "scikit-optimize",
 ]
+docs = [
+    "ipykernel",
+    "matplotlib>=3.5",
+    "myst-parser",
+    "nbsphinx",
+    "nbsphinx_link",
+    "pylatexenc>=2.10",
+    "sphinx",
+    "sphinxcontrib-spelling",
+    "sphinx-rtd-theme<3.0.0",
+    "sphinx-sitemap",
+    "jupyter-sphinx",
+]
 
 [project.urls]
 Homepage = "https://github.com/sQUlearn/squlearn"

pyproject.toml

@@ -179,7 +179,7 @@ def automated_pruning(
     verbose: int = 1,
     seed: Union[int, None] = None,
 ) -> PrunedEncodingCircuit:
-    """
+    r"""
     Function for automated pruning of the parameters in the inputted parameterized quantum circuit.
 
     The algorithms for the automated pruning is based on

src/squlearn/encoding_circuit/pruned_encoding_circuit.py

@@ -866,7 +866,7 @@ def _set_outer_kernel(self, outer_kernel: Union[str, OuterKernelBase], **kwargs)
 
 
 class GaussianOuterKernel(OuterKernelBase):
-    """
+    r"""
     Implementation of the Gaussian outer kernel:
 
     .. math::

src/squlearn/kernel/matrix/projected_quantum_kernel.py

@@ -11,7 +11,7 @@
 
 
 class QKRR(BaseEstimator, RegressorMixin):
-    """
+    r"""
     Quantum Kernel Ridge Regression.
 
     This class implements the Quantum Kernel Ridge Regression analogous to KRR [1] in scikit-learn

src/squlearn/kernel/ml/qkrr.py

@@ -1604,7 +1604,7 @@ def observables(self):
 
     @property
     def parameters(self):
-        """Parameters of the quantum circuits.
+        r"""Parameters of the quantum circuits.
 
         Returns:
             Parameters, where ``parameters[i][j]`` is the j-\ :spelling:word:`th` parameter of the