Added parameter doc for SVM algos
@@ -203,6 +203,16 @@ the feature space. The method used falls in the category of what is called _supe
phase_ (where the kernel is calculated and the support vectors obtained) and a _test or classification phase_ (where new
labeless data is classified according to the solution found in the training phase).
+SVM_QKernel can be configured with the following parameters:
+
+* `num_qubits`=*integer 2 to n, defaults to 2*
+
+ Number of qubits to use for the problem.
+
+* `print_info`=*False* | True
+
+ Whether to print additional information or not when the algorithm is running.
+
## SVM_Variational
@@ -210,6 +220,20 @@ Just like SVM_Kernel, the SVM_Variational algorithm applies to classification pr
computing the kernel is not efficient classically. SVM_Variational solves such problems in a quantum processor by
variational method that optimizes a parameterized quantum circuit to provide a solution that cleanly separates the data.
+SVM_Variational can be configured with the following parameters:
+
+* `num_qubits`=*integer 2 to n, defaults to 2*
+
+ Number of qubits to use for the problem.
+
+* `circuit_depth`=*integer 3 to n, defaults to 3*
+
+ Circuit depth to use for the variational form.
+
+* `print_info`=*False* | True
+
+ Whether to print additional information or not when the algorithm is running.
+
## ExactEigensolver
