diff --git a/src/qibotn/eval.py b/src/qibotn/eval.py
index 39cca9ce..a283ff34 100644
--- a/src/qibotn/eval.py
+++ b/src/qibotn/eval.py
@@ -9,19 +9,19 @@
 from qibotn.mps_contraction_helper import MPSContractionHelper
 
 
-def eval(qibo_circ, datatype):
+def dense_vector_tn(qibo_circ, datatype):
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(*myconvertor.state_vector_operands())
 
 
-def eval_expectation(qibo_circ, datatype):
+def expectation_tn(qibo_circ, datatype):
     myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
     return contract(
         *myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
     )
 
 
-def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
+def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
     """Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI.
     The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
     The pathfinder looks at user defined number of samples (n_samples) iteratively to select the least costly contraction path. This is sped up with multi thread.
@@ -117,7 +117,7 @@ def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
     return result, rank
 
 
-def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
+def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
     from mpi4py import MPI  # this line initializes MPI
     import socket
     from cuquantum import Network
@@ -203,7 +203,7 @@ def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
     return result, rank
 
 
-def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
+def expectation_tn_nccl(qibo_circ, datatype, n_samples=8):
     from mpi4py import MPI  # this line initializes MPI
     import socket
     from cuquantum import Network
@@ -290,7 +290,7 @@ def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
     return result, rank
 
 
-def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
+def expectation_tn_MPI(qibo_circ, datatype, n_samples=8):
     from mpi4py import MPI  # this line initializes MPI
     import socket
     from cuquantum import Network
@@ -369,7 +369,7 @@ def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
     return result, rank
 
 
-def eval_mps(qibo_circ, gate_algo, datatype):
+def dense_vector_mps(qibo_circ, gate_algo, datatype):
     myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
     mps_helper = MPSContractionHelper(myconvertor.num_qubits)
 
diff --git a/tests/test_cuquantum_cutensor_backend.py b/tests/test_cuquantum_cutensor_backend.py
index 58020742..3de5c17b 100644
--- a/tests/test_cuquantum_cutensor_backend.py
+++ b/tests/test_cuquantum_cutensor_backend.py
@@ -42,7 +42,7 @@ def test_eval(nqubits: int, dtype="complex128"):
 
     # Test Cuquantum
     cutn_time, result_tn = time(
-        lambda: qibotn.cutn.eval(qibo_circ, dtype).flatten())
+        lambda: qibotn.eval.dense_vector_tn(qibo_circ, dtype).flatten())
 
     assert 1e-2 * qibo_time < cutn_time < 1e2 * qibo_time
     assert np.allclose(
@@ -78,7 +78,7 @@ def test_mps(nqubits: int, dtype="complex128"):
                  }}
 
     cutn_time, result_tn = time(
-        lambda: qibotn.eval.eval_mps(circ_qibo, gate_algo, dtype).flatten())
+        lambda: qibotn.eval.dense_vector_mps(circ_qibo, gate_algo, dtype).flatten())
 
     print(
         f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}")