Changes suggested by Iakov.

docs/modules/tnstate.rst

@@ -9,7 +9,7 @@ Simulation
 
 .. autofunction:: pytket.extensions.cutensornet.tnstate.simulate
 
-.. autoenum:: pytket.extensions.cutensornet.tnstate.ContractionAlg()
+.. autoenum:: pytket.extensions.cutensornet.tnstate.SimulationAlgorithm()
     :members:
 
 .. autoclass:: pytket.extensions.cutensornet.tnstate.Config()

pytket/extensions/cutensornet/tnstate/__init__.py

@@ -19,7 +19,7 @@
 """
 
 from .general import CuTensorNetHandle, Config, TNState
-from .simulation import ContractionAlg, simulate, prepare_circuit_mps
+from .simulation import SimulationAlgorithm, simulate, prepare_circuit_mps
 
 from .mps import DirMPS, MPS
 from .mps_gate import MPSxGate

pytket/extensions/cutensornet/tnstate/general.py

@@ -170,6 +170,10 @@ def __init__(
                 UserWarning,
             )
 
+        if leaf_size >= 65:  # Imposed to avoid bond ID collisions
+            # More than 20 qubits is already unreasonable for a leaf anyway
+            raise ValueError("Maximum allowed leaf_size is 65.")
+
         self.leaf_size = leaf_size
         self.k = k
         self.optim_delta = 1e-5
@@ -394,29 +398,3 @@ def copy(self) -> TNState:
     @abstractmethod
     def _flush(self) -> None:
         raise NotImplementedError(f"Method not implemented in {type(self).__name__}.")
-
-
-def _safe_qr(
-    libhandle: CuTensorNetHandle, indices: str, T: cp.ndarray
-) -> tuple[cp.ndarray, cp.ndarray]:
-    """Wrapper of cuTensorNet QR decomposition to work around a bug in 23.6.0.
-
-    The bug has been reported https://github.com/NVIDIA/cuQuantum/discussions/96.
-    """
-    try:
-        Q, R = tensor.decompose(
-            indices,
-            T,
-            method=tensor.QRMethod(),
-            options={"handle": libhandle.handle, "device_id": libhandle.device_id},
-        )
-    except cutn.cuTensorNetError:
-        Q, S, R = tensor.decompose(
-            indices,
-            T,
-            method=tensor.SVDMethod(partition="U"),  # Contracts S to Q
-            options={"handle": libhandle.handle, "device_id": libhandle.device_id},
-        )
-        assert S is None
-
-    return (Q, R)

pytket/extensions/cutensornet/tnstate/mps.py

@@ -93,24 +93,24 @@ def __init__(
 
         n_tensors = len(qubits)
         if n_tensors == 0:  # There's no initialisation to be done
-            return None
+            pass
         elif n_tensors == 1:
             raise ValueError("Please, provide at least two qubits.")
-
-        self.qubit_position = {q: i for i, q in enumerate(qubits)}
-
-        # Create the list of tensors
-        self.tensors: list[Tensor] = []
-        self.canonical_form = {i: None for i in range(n_tensors)}
-
-        # Append each of the tensors initialised in state |0>
-        m_shape = (1, 1, 2)  # Two virtual bonds (dim=1) and one physical
-        for i in range(n_tensors):
-            m_tensor = cp.empty(m_shape, dtype=self._cfg._complex_t)
-            # Initialise the tensor to ket 0
-            m_tensor[0][0][0] = 1
-            m_tensor[0][0][1] = 0
-            self.tensors.append(m_tensor)
+        else:
+            self.qubit_position = {q: i for i, q in enumerate(qubits)}
+
+            # Create the list of tensors
+            self.tensors: list[Tensor] = []
+            self.canonical_form = {i: None for i in range(n_tensors)}
+
+            # Append each of the tensors initialised in state |0>
+            m_shape = (1, 1, 2)  # Two virtual bonds (dim=1) and one physical
+            for i in range(n_tensors):
+                m_tensor = cp.empty(m_shape, dtype=self._cfg._complex_t)
+                # Initialise the tensor to ket 0
+                m_tensor[0][0][0] = 1
+                m_tensor[0][0][1] = 0
+                self.tensors.append(m_tensor)
 
     def is_valid(self) -> bool:
         """Verify that the MPS object is valid.

pytket/extensions/cutensornet/tnstate/simulation.py

@@ -33,7 +33,7 @@
 from .ttn_gate import TTNxGate
 
 
-class ContractionAlg(Enum):
+class SimulationAlgorithm(Enum):
     """An enum to refer to the TNState contraction algorithm.
 
     Each enum value corresponds to the class with the same name; see its docs for
@@ -48,7 +48,7 @@ class ContractionAlg(Enum):
 def simulate(
     libhandle: CuTensorNetHandle,
     circuit: Circuit,
-    algorithm: ContractionAlg,
+    algorithm: SimulationAlgorithm,
     config: Config,
 ) -> TNState:
     """Simulates the circuit and returns the ``TNState`` representing the final state.
@@ -65,7 +65,7 @@ def simulate(
         libhandle: The cuTensorNet library handle that will be used to carry out
             tensor operations.
         circuit: The pytket circuit to be simulated.
-        algorithm: Choose between the values of the ``ContractionAlg`` enum.
+        algorithm: Choose between the values of the ``SimulationAlgorithm`` enum.
         config: The configuration object for simulation.
 
     Returns:
@@ -77,23 +77,23 @@ def simulate(
     logger.info(
         "Ordering the gates in the circuit to reduce canonicalisation overhead."
     )
-    if algorithm == ContractionAlg.MPSxGate:
+    if algorithm == SimulationAlgorithm.MPSxGate:
         tnstate = MPSxGate(  # type: ignore
             libhandle,
             circuit.qubits,
             config,
         )
         sorted_gates = _get_sorted_gates(circuit, algorithm)
 
-    elif algorithm == ContractionAlg.MPSxMPO:
+    elif algorithm == SimulationAlgorithm.MPSxMPO:
         tnstate = MPSxMPO(  # type: ignore
             libhandle,
             circuit.qubits,
             config,
         )
         sorted_gates = _get_sorted_gates(circuit, algorithm)
 
-    elif algorithm == ContractionAlg.TTNxGate:
+    elif algorithm == SimulationAlgorithm.TTNxGate:
         qubit_partition = _get_qubit_partition(circuit, config.leaf_size)
         tnstate = TTNxGate(  # type: ignore
             libhandle,
@@ -226,7 +226,7 @@ def _get_qubit_partition(
 
 def _get_sorted_gates(
     circuit: Circuit,
-    algorithm: ContractionAlg,
+    algorithm: SimulationAlgorithm,
     qubit_partition: Optional[dict[int, list[Qubit]]] = None,
 ) -> list[Command]:
     """Sorts the list of gates so that there's less canonicalisation during simulation.
@@ -251,7 +251,7 @@ def _get_sorted_gates(
     remaining = set(range(len(all_gates)))
 
     # Do some precomputation depending on the algorithm
-    if algorithm in [ContractionAlg.TTNxGate]:
+    if algorithm in [SimulationAlgorithm.TTNxGate]:
         if qubit_partition is None:
             raise RuntimeError("You must provide a qubit partition!")
 
@@ -260,7 +260,7 @@ def _get_sorted_gates(
             for q in qubits:
                 leaf_of_qubit[q] = leaf
 
-    elif algorithm in [ContractionAlg.MPSxGate, ContractionAlg.MPSxMPO]:
+    elif algorithm in [SimulationAlgorithm.MPSxGate, SimulationAlgorithm.MPSxMPO]:
         idx_of_qubit = {q: i for i, q in enumerate(circuit.qubits)}
 
     else:
@@ -301,14 +301,17 @@ def _get_sorted_gates(
             gate_qubits = all_gates[gate_idx].qubits
 
             # Criterion for distance depends on the simulation algorithm
-            if algorithm in [ContractionAlg.TTNxGate]:
+            if algorithm in [SimulationAlgorithm.TTNxGate]:
                 gate_distance[gate_idx] = max(  # Max common ancestor distance
                     leaf_of_qubit[last_qubits[0]] ^ leaf_of_qubit[gate_qubits[0]],
                     leaf_of_qubit[last_qubits[0]] ^ leaf_of_qubit[gate_qubits[1]],
                     leaf_of_qubit[last_qubits[1]] ^ leaf_of_qubit[gate_qubits[0]],
                     leaf_of_qubit[last_qubits[1]] ^ leaf_of_qubit[gate_qubits[1]],
                 )
-            elif algorithm in [ContractionAlg.MPSxGate, ContractionAlg.MPSxMPO]:
+            elif algorithm in [
+                SimulationAlgorithm.MPSxGate,
+                SimulationAlgorithm.MPSxMPO,
+            ]:
                 gate_distance[gate_idx] = max(  # Max linear distance between qubits
                     abs(idx_of_qubit[last_qubits[0]] - idx_of_qubit[gate_qubits[0]]),
                     abs(idx_of_qubit[last_qubits[0]] - idx_of_qubit[gate_qubits[1]]),