doc: improve docstrings related to TensorNetworkResult

src/qibotn/backends/qmatchatea.py

@@ -126,9 +126,6 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
             prob_type = "U"
             prob_kwargs = {"num_samples": 500}
 
-        # To be sure the setup is correct and no modifications have been done
-        self._setup_qmatchatea_backend()
-
         # TODO: check
         circuit = self._qibocirc_to_qiskitcirc(circuit)
         run_qk_params = qmatchatea.preprocessing.qk_transpilation_params(False)
@@ -189,7 +186,9 @@ class QMatchaTeaBackend(QibotnBackend, NumpyBackend):
                 (e.g., `X(0)*Y(1)` or `Z(0)*Z(1) + 1.5*Y(2)`).
 
         Returns:
-            qmatchatea.SimulationResult [TEMPORARY]
+            qibotn.TensorNetworkResult class, providing methods to retrieve
+            probabilities, frequencies and state always according to the chosen
+            simulation setup.
         """
 
         # From Qibo to Qiskit

src/qibotn/result.py

@@ -10,6 +10,20 @@ from qibotn.backends.abstract import QibotnBackend
 
 @dataclass
 class TensorNetworkResult:
+    """
+    Object to store and process the output of a Tensor Network simulation of a quantum circuit.
+
+    Args:
+        nqubits (int): number of qubits involved in the simulation;
+        backend (QibotnBackend): specific backend on which the simulation has been performed;
+        measures (dict): measures (if performed) during the tensor network simulation;
+        measured_probabilities (Union[dict, ndarray]): probabilities of the final state
+            according to the simulation;
+        prob_type (str): string identifying the method used to compute the probabilities.
+            Especially useful in case the `QmatchateaBackend` is selected.
+        statevector (ndarray): if computed, the reconstructed statevector.
+    """
+
     nqubits: int
     backend: QibotnBackend
     measures: dict