diff --git a/src/qibotn/QiboCircuitConvertor.py b/src/qibotn/QiboCircuitConvertor.py
index 18c79fd..353a054 100644
--- a/src/qibotn/QiboCircuitConvertor.py
+++ b/src/qibotn/QiboCircuitConvertor.py
@@ -19,33 +19,13 @@ class QiboCircuitToEinsum:
     def __init__(self, circuit, dtype="complex128"):
         self.backend = cp
         self.dtype = getattr(self.backend, dtype)
-
-        self.gate_tensors = []
-        gates_qubits = []
-
-        for gate in circuit.queue:
-            gate_qubits = gate.control_qubits + gate.target_qubits
-            gates_qubits.extend(gate_qubits)
-
-            # self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on
-            # https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32
-            required_shape = self.op_shape_from_qubits(len(gate_qubits))
-            self.gate_tensors.append(
-                (
-                    cp.asarray(gate.matrix).reshape(required_shape),
-                    gate_qubits,
-                )
-            )
-
-        # self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit.
-        self.active_qubits = np.unique(gates_qubits)
+        self.init_basis_map(self.backend, dtype)
+        self.init_intermediate_circuit(circuit)
 
     def state_vector_operands(self):
-        input_tensor_count = len(self.active_qubits)
+        input_bitstring = "0" * len(self.active_qubits)
 
-        input_operands = self._get_bitstring_tensors(
-            "0" * input_tensor_count, self.dtype, backend=self.backend
-        )
+        input_operands = self._get_bitstring_tensors(input_bitstring)
 
         (
             mode_labels,
@@ -69,21 +49,13 @@ class QiboCircuitToEinsum:
         return operand_exp_interleave
 
     def _init_mode_labels_from_qubits(self, qubits):
-        frontier_dict = {}
         n = len(qubits)
-        for x in range(n):
-            frontier_dict[qubits[x]] = x
-        return [[i] for i in range(n)], frontier_dict, n
+        frontier_dict = {qubits[x]: x for x in range(n)}
+        mode_labels = [[i] for i in range(n)]
+        return mode_labels, frontier_dict, n
 
-    def _get_bitstring_tensors(self, bitstring, dtype=np.complex128, backend=cp):
-        asarray = backend.asarray
-        state_0 = asarray([1, 0], dtype=dtype)
-        state_1 = asarray([0, 1], dtype=dtype)
-
-        basis_map = {"0": state_0, "1": state_1}
-
-        operands = [basis_map[ibit] for ibit in bitstring]
-        return operands
+    def _get_bitstring_tensors(self, bitstring):
+        return [self.basis_map[ibit] for ibit in bitstring]
 
     def _parse_gates_to_mode_labels_operands(
         self, gates, qubits_frontier, next_frontier
@@ -108,3 +80,31 @@ class QiboCircuitToEinsum:
         (qubit_states,input_output) * qubits_involved
         """
         return (2, 2) * nqubits
+
+    def init_intermediate_circuit(self, circuit):
+        self.gate_tensors = []
+        gates_qubits = []
+
+        for gate in circuit.queue:
+            gate_qubits = gate.control_qubits + gate.target_qubits
+            gates_qubits.extend(gate_qubits)
+
+            # self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on
+            # https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32
+            required_shape = self.op_shape_from_qubits(len(gate_qubits))
+            self.gate_tensors.append(
+                (
+                    cp.asarray(gate.matrix).reshape(required_shape),
+                    gate_qubits,
+                )
+            )
+
+        # self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit.
+        self.active_qubits = np.unique(gates_qubits)
+
+    def init_basis_map(self, backend, dtype):
+        asarray = backend.asarray
+        state_0 = asarray([1, 0], dtype=dtype)
+        state_1 = asarray([0, 1], dtype=dtype)
+
+        self.basis_map = {"0": state_0, "1": state_1}