[Fix] Handle nested parameter dicts (#482)

pyproject.toml

@@ -45,7 +45,7 @@ dependencies = [
     "jsonschema",
     "nevergrad",
     "scipy",
-    "pyqtorch==1.2.3",
+    "pyqtorch==1.2.4",
     "pyyaml",
     "matplotlib",
     "Arpeggio==2.0.2",

qadence/backends/pyqtorch/backend.py

@@ -12,6 +12,7 @@
 from qadence.backend import Backend as BackendInterface
 from qadence.backend import ConvertedCircuit, ConvertedObservable
 from qadence.backends.utils import (
+    infer_batchsize,
     pyqify,
     to_list_of_dicts,
     unpyqify,
@@ -31,7 +32,6 @@
     transpile,
 )
 from qadence.types import BackendName, Endianness, Engine
-from qadence.utils import infer_batchsize
 
 from .config import Configuration, default_passes
 from .convert_ops import convert_block

qadence/backends/utils.py

@@ -143,9 +143,22 @@ def validate_state(state: Tensor, n_qubits: int) -> None:
         )
 
 
-def infer_batchsize(param_values: ParamDictType = None) -> int:
+def infer_batchsize(param_values: dict[str, Tensor] = None) -> int:
     """Infer the batch_size through the length of the parameter tensors."""
-    return max([len(tensor) for tensor in param_values.values()]) if param_values else 1
+    try:
+        return (
+            max(
+                [
+                    len(tensor_or_dict)
+                    for tensor_or_dict in param_values.values()
+                    if isinstance(tensor_or_dict, Tensor)
+                ]
+            )
+            if param_values
+            else 1
+        )
+    except Exception:
+        return 1
 
 
 # The following functions can be used to compute potentially higher order gradients using pyqtorch's

qadence/utils.py

@@ -234,18 +234,12 @@ def is_qadence_shape(state: ArrayLike, n_qubits: int) -> bool:
     return state.shape[1] == 2**n_qubits  # type: ignore[no-any-return]
 
 
-def infer_batchsize(param_values: dict[str, Tensor] = None) -> int:
-    """Infer the batch_size through the length of the parameter tensors."""
-    try:
-        return max([len(tensor) for tensor in param_values.values()]) if param_values else 1
-    except Exception:
-        return 1
-
-
 def validate_values_and_state(
     state: ArrayLike | None, n_qubits: int, param_values: dict[str, Tensor] = None
 ) -> None:
     if state is not None:
+        from qadence.backends.utils import infer_batchsize
+
         if isinstance(state, Tensor):
             if state is not None:
                 batch_size_state = (