add backend file

src/qibotn/backends.py

@@ -0,0 +1,69 @@
+from qibo.backends import NumpyBackend
+from qibo.config import raise_error
+from qibotn import cutn
+from qibotn import quimb
+from qibo.states import CircuitResult
+
+
+class QiboTNBackend(NumpyBackend):
+    def __init__(self, platform):
+        super().__init__()
+        self.name = "qibotn"
+        if (
+            platform == "cu_tensornet"
+            or platform == "cu_mps"
+            or platform == "qu_tensornet"
+        ):  # pragma: no cover
+            self.platform = platform
+        else:
+            raise_error(
+                NotImplementedError, "QiboTN cannot support the specified backend."
+            )
+
+    def apply_gate(self, gate, state, nqubits):  # pragma: no cover
+        raise_error(NotImplementedError, "QiboTN cannot apply gates directly.")
+
+    def apply_gate_density_matrix(self, gate, state, nqubits):  # pragma: no cover
+        raise_error(NotImplementedError, "QiboTN cannot apply gates directly.")
+
+    def assign_measurements(self, measurement_map, circuit_result):
+        raise_error(NotImplementedError, "Not implemented in QiboTN.")
+
+    def execute_circuit(
+        self, circuit, initial_state=None, nshots=None, return_array=False
+    ):  # pragma: no cover
+        """Executes a quantum circuit.
+
+        Args:
+            circuit (:class:`qibo.models.circuit.Circuit`): Circuit to execute.
+            initial_state (:class:`qibo.models.circuit.Circuit`): Circuit to prepare the initial state.
+                If ``None`` the default ``|00...0>`` state is used.
+
+        Returns:
+            xxx.
+
+        """
+        if initial_state is not None:
+            raise_error(NotImplementedError, "QiboTN cannot support initial state.")
+
+        if self.platform == "cu_tensornet":
+            state = cutn.eval(circuit, self.dtype)
+
+        if self.platform == "cu_mps":
+            gate_algo = {
+                "qr_method": False,
+                "svd_method": {
+                    "partition": "UV",
+                    "abs_cutoff": 1e-12,
+                },
+            }  # make this user input
+            state = cutn.eval_mps(circuit, gate_algo, self.dtype)
+
+        if self.platform == "qu_tensornet":
+            state = quimb.eval(circuit.to_qasm(), initial_state, backend="numpy")
+
+        if return_array:
+            return state.flatten()
+        else:
+            circuit._final_state = CircuitResult(self, circuit, state.flatten(), nshots)
+            return circuit._final_state