feat: dynamically inheriting with quimb

src/qibotn/backends/__init__.py

@@ -13,7 +13,7 @@ class MetaBackend:
     """Meta-backend class which takes care of loading the qibotn backends."""
 
     @staticmethod
-    def load(platform: str, runcard: dict = None) -> QibotnBackend:
+    def load(platform: str, runcard: dict = None, **kwargs) -> QibotnBackend:
         """Loads the backend.
 
         Args:
@@ -26,7 +26,8 @@ class MetaBackend:
         if platform == "cutensornet":  # pragma: no cover
             return CuTensorNet(runcard)
         elif platform == "quimb":  # pragma: no cover
-            return QuimbBackend()
+            quimb_backend = kwargs.get("quimb_backend", "numpy")
+            return QuimbBackend(quimb_backend)
         elif platform == "qmatchatea":  # pragma: no cover
             from qibotn.backends.qmatchatea import QMatchaTeaBackend
 

src/qibotn/backends/quimb.py

@@ -39,10 +39,12 @@ GATE_MAP = {
 }
 
 
-class QuimbBackend(QibotnBackend, NumpyBackend):
+# class QuimbBackend(QibotnBackend, NumpyBackend):
+if not __name__ == "__main__":
 
     def __init__(self, engine="numpy"):
-        super().__init__()
+        # uper().__init__()
+        super(self.__class__, self).__init__()
 
         self.name = "qibotn"
         self.platform = "quimb"
@@ -85,6 +87,7 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
             contractions_optimizer: str, optional
                 The contractions_optimizer to use for the quimb tensor network simulation.
         """
+        # this is not really working because it does not change the inheritance
         if quimb_backend == "jax":
             import jax.numpy as jnp
 
@@ -142,7 +145,6 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
             - If `initial_state` is provided, it must be compatible with the MPS ansatz.
             - The `nshots` parameter enables sampling from the circuit's output distribution. If not specified, the full statevector is computed.
         """
-
         if initial_state is not None and self.ansatz == "MPS":
             initial_state = qtn.tensor_1d.MatrixProductState.from_dense(
                 initial_state, 2
@@ -304,3 +306,32 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
         for c in op_str[1:]:
             op = op & qu.pauli(c)
         return op
+
+
+def QuimbBackend(quimb_backend: str = "numpy") -> QibotnBackend:
+    bases = (QibotnBackend,)
+    methods = {
+        "__init__": __init__,
+        "configure_tn_simulation": configure_tn_simulation,
+        "setup_backend_specifics": setup_backend_specifics,
+        "execute_circuit": execute_circuit,
+        "expectation_observable_symbolic_from_state": expectation_observable_symbolic_from_state,
+        "_qibo_circuit_to_quimb": _qibo_circuit_to_quimb,
+        "_string_to_quimb_operator": _string_to_quimb_operator,
+    }
+    if quimb_backend == "numpy":
+        from qibo.backends import NumpyBackend
+
+        bases += (NumpyBackend,)
+    elif quimb_backend == "torch":
+        from qiboml.backends import PyTorchBackend
+
+        bases += (PyTorchBackend,)
+    elif quimb_backend == "jax":
+        from qiboml.backends import JaxBackend
+
+        bases += (JaxBackend,)
+    else:
+        raise_error(ValueError, f"Unsupported quimb backend: {quimb_backend}")
+
+    return type("QuimbBackend", bases, methods)(quimb_backend)