Update docstrings and refine

src/qibotn/eval.py

@@ -9,14 +9,31 @@ from qibotn.mps_contraction_helper import MPSContractionHelper
 
 def dense_vector_tn(qibo_circ, datatype):
     """Convert qibo circuit to tensornet (TN) format and perform contraction to
-    dense vector."""
+    dense vector.
+    
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+
+    Returns:
+        Dense vector of quantum circuit.
+    """
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(*myconvertor.state_vector_operands())
 
 
 def expectation_pauli_tn(qibo_circ, datatype, pauli_string_pattern):
     """Convert qibo circuit to tensornet (TN) format and perform contraction to
-    expectation of given Pauli string."""
+    expectation of given Pauli string.
+                 
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        pauli_string_pattern(str): pauli string pattern.
+        
+    Returns:
+        Expectation of quantum circuit due to pauli string.
+    """
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(
         *myconvertor.expectation_operands(
@@ -35,6 +52,14 @@ def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
     the least costly contraction path. This is sped up with multi
     thread. After pathfinding the optimal path is used in the actual
     contraction to give a dense vector representation of the TN.
+    
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        n_samples(int): Number of samples for pathfinding.
+        
+    Returns:
+        Dense vector of quantum circuit.
     """
 
     from cuquantum import Network
@@ -102,6 +127,14 @@ def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
     the least costly contraction path. This is sped up with multi
     thread. After pathfinding the optimal path is used in the actual
     contraction to give a dense vector representation of the TN.
+         
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        n_samples(int): Number of samples for pathfinding.
+        
+    Returns:
+        Dense vector of quantum circuit.
     """
     from cupy.cuda import nccl
     from cuquantum import Network
@@ -183,6 +216,15 @@ def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_sampl
     select the least costly contraction path. This is sped up with multi
     thread. After pathfinding the optimal path is used in the actual
     contraction to give an expectation value.
+             
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        pauli_string_pattern(str): pauli string pattern.
+        n_samples(int): Number of samples for pathfinding.
+        
+    Returns:
+        Expectation of quantum circuit due to pauli string.
     """
     from cupy.cuda import nccl
     from cuquantum import Network
@@ -266,6 +308,15 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_sample
     select the least costly contraction path. This is sped up with multi
     thread. After pathfinding the optimal path is used in the actual
     contraction to give an expectation value.
+                 
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        pauli_string_pattern(str): pauli string pattern.
+        n_samples(int): Number of samples for pathfinding.
+        
+    Returns:
+        Expectation of quantum circuit due to pauli string.
     """
     from cuquantum import Network
     from mpi4py import MPI  # this line initializes MPI
@@ -326,7 +377,16 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_sample
 
 def dense_vector_mps(qibo_circ, gate_algo, datatype):
     """Convert qibo circuit to matrix product state (MPS) format and perform
-    contraction to dense vector."""
+    contraction to dense vector.
+                 
+    Parameters:
+        qibo_circ: The quantum circuit object.
+        gate_algo(dict): Dictionary for SVD and QR settings.
+        datatype (str): Either single ("complex64") or double (complex128) precision.
+        
+    Returns:
+        Dense vector of quantum circuit.
+    """
     myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
     mps_helper = MPSContractionHelper(myconvertor.num_qubits)
 
@@ -339,6 +399,13 @@ def pauli_string_gen(nqubits, pauli_string_pattern):
     """Used internally to generate the string based on given pattern and number
     of qubit.
 
+    Parameters:
+        nqubits(int): Number of qubits of Quantum Circuit
+        pauli_string_pattern(str): Strings representing sequence of pauli gates.
+        
+    Returns:
+        String representation of the actual pauli string from the pattern.
+        
     Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX
     """
     if nqubits <= 0: