fix See Also section in docstrings (#373)

Author: MoritzWillmann

examples/kernel/example_projected_kernel.ipynb

@@ -77,8 +77,7 @@ class ODELoss(KernelLossBase):
             eta=1.2,
         )
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QKODE : Quantum Kernel for ODE solving.
 
     References

src/squlearn/kernel/loss/ode_loss.py

@@ -33,8 +33,7 @@ class QGPC(GaussianProcessClassifier, SerializableModelMixin):
             (``num_qubits=``), or (if supported) the number of layers (``num_layers=``)
             of the underlying encoding circuit.
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QSVC : Quantum Support Vector classification.
 
     References

src/squlearn/kernel/qgpc.py

@@ -60,8 +60,7 @@ class QGPR(BaseEstimator, RegressorMixin, SerializableModelMixin):
             (``num_qubits=``), or (if supported) the number of layers (``num_layers=``)
             of the underlying encoding circuit.
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QKRR : Quantum Gaussian Process regression.
         squlearn.kernel.QSVR : Quantum Support Vector regression.
 

src/squlearn/kernel/qgpr.py

@@ -56,8 +56,7 @@ class QKODE(QKRR):
         k_testtrain (np.ndarray) :
             Kernel matrix of shape (n_test, n_train) which is evaluated at the predict step.
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.loss.ODELoss : Loss function for ODEs.
 
     References

src/squlearn/kernel/qkode.py

@@ -58,8 +58,7 @@ class QKRR(BaseEstimator, RegressorMixin, SerializableModelMixin):
         k_testtrain (np.ndarray) :
             Kernel matrix of shape (n_test, n_train) which is evaluated at the predict step
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QGPR : Quantum Gaussian Process regression.
         squlearn.kernel.QSVR : Quantum Support Vector regression.
 

src/squlearn/kernel/qkrr.py

@@ -38,8 +38,7 @@ class QSVC(SVC, SerializableModelMixin):
             adjusted via kwargs such as the number of qubits (``num_qubits``), or (if supported)
             the number of layers (``num_layers``).
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QSVR : Quantum Support Vector Regression
 
     **Example**

src/squlearn/kernel/qsvc.py

@@ -39,8 +39,7 @@ class QSVR(SVR, SerializableModelMixin):
             underlying encoding circuit can be adjusted via kwargs such as the number of qubits
             (``num_qubits``), or (if supported) the number of layers (``num_layers``).
 
-    See Also
-    --------
+    See Also:
         squlearn.kernel.QSVC : Quantum Support Vector Classification
 
     **Example**

src/squlearn/kernel/qsvr.py

@@ -77,8 +77,7 @@ class QNNClassifier(BaseQNN, ClassifierMixin):
             specified, the estimator will used. Possible values are ``"estimator"`` or
             ``"sampler"``.
 
-    See Also
-    --------
+    See Also:
         squlearn.qnn.QNNRegressor : Quantum Neural Network for Regression.
 
     **Example**

src/squlearn/qnn/qnnc.py

@@ -77,8 +77,7 @@ class QNNRegressor(BaseQNN, RegressorMixin):
             specified, the estimator will used. Possible values are ``"estimator"`` or
             ``"sampler"``.
 
-    See Also
-    --------
+    See Also:
         squlearn.qnn.QNNClassifier : Quantum Neural Network for Classification.
 
     **Example**