Changes suggested by Iakov

pytket/extensions/cutensornet/tnstate/mps_mpo.py

@@ -388,7 +388,7 @@ def update_sweep_cache(pos: int, direction: DirMPS) -> None:
             T = cq.contract(
                 *interleaved_rep,
                 options={"handle": self._lib.handle, "device_id": self._lib.device_id},
-                optimize={"samples": 1},
+                optimize={"samples": 0},
             )
             if direction == DirMPS.LEFT:
                 r_cached_tensors.append(T)
@@ -450,7 +450,7 @@ def update_variational_tensor(
             F = cq.contract(
                 *interleaved_rep,
                 options={"handle": self._lib.handle, "device_id": self._lib.device_id},
-                optimize={"samples": 1},
+                optimize={"samples": 0},
             )
 
             # Get the fidelity

pytket/extensions/cutensornet/tnstate/ttn.py

@@ -569,7 +569,7 @@ def vdot(self, other: TTN) -> complex:  # type: ignore
         result = cq.contract(
             *interleaved_rep,
             options={"handle": self._lib.handle, "device_id": self._lib.device_id},
-            optimize={"samples": 1},  # There is little to no optimisation to be done
+            optimize={"samples": 0},  # There is little to no optimisation to be done
         )
 
         self._logger.debug(f"Result from vdot={result}")
@@ -672,7 +672,7 @@ def get_statevector(self) -> np.ndarray:
         result_tensor = cq.contract(
             *interleaved_rep,
             options={"handle": self._lib.handle, "device_id": self._lib.device_id},
-            optimize={"samples": 1},  # There is little to no optimisation to be done
+            optimize={"samples": 0},  # There is little to no optimisation to be done
         )
 
         # Convert to numpy vector and flatten
@@ -712,7 +712,7 @@ def get_amplitude(self, state: int) -> complex:
         result = cq.contract(
             *interleaved_rep,
             options={"handle": self._lib.handle, "device_id": self._lib.device_id},
-            optimize={"samples": 1},  # There is little to no optimisation to be done
+            optimize={"samples": 0},  # There is little to no optimisation to be done
         )
 
         self._logger.debug(f"Amplitude of state {state} is {result}.")
@@ -773,7 +773,7 @@ def get_dimension(self, path: RootPath, direction: DirTTN) -> int:
             direction: The direction of the bond.
 
         Returns:
-            The dimension of the bond between the node and its parent.
+            The dimension of the specified bond.
 
         Raises:
             ValueError: If ``path`` is not in the TTN.

pytket/extensions/cutensornet/tnstate/ttn_gate.py

@@ -87,7 +87,7 @@ def _apply_1q_gate(self, qubit: Qubit, gate: Op) -> TTNxGate:
     def _apply_2q_gate(self, q0: Qubit, q1: Qubit, gate: Op) -> TTNxGate:
         """Applies the 2-qubit gate to the TTN.
 
-        Truncation is automatically applied according to the paremeters
+        Truncation is automatically applied according to the parameters
         in the ``Config`` object passed to this ``TTN``.
         The TTN is converted to canonical form before truncating.
 
@@ -130,7 +130,7 @@ def _apply_2q_gate(self, q0: Qubit, q1: Qubit, gate: Op) -> TTNxGate:
             node_bonds = aux_bonds.copy()
             node_bonds[bond_q0] = "i0"
             node_bonds[bond_q1] = "i1"
-            result_bonds = aux_bonds.copy()
+            result_bonds = aux_bonds
             result_bonds[bond_q0] = "o0"
             result_bonds[bond_q1] = "o1"
 
@@ -237,7 +237,7 @@ def _apply_2q_gate(self, q0: Qubit, q1: Qubit, gate: Op) -> TTNxGate:
             aux_bonds = [f"q{x}" for x in range(n_qbonds)] + ["p"]
             node_bonds = aux_bonds.copy()
             node_bonds[bond] = "a"
-            result_bonds = aux_bonds.copy()
+            result_bonds = aux_bonds
             result_bonds[bond] = "b"
             result_bonds[-1] = "f"