Merge branch 'main' into gt/ml-flow

qadence/backends/pyqtorch/convert_ops.py

@@ -201,7 +201,12 @@ def __init__(
         elif isinstance(block.generator, Tensor):
             m = block.generator.to(dtype=cdouble)
             hmat = block_to_tensor(
-                MatrixBlock(m, qubit_support=block.qubit_support),
+                MatrixBlock(
+                    m,
+                    qubit_support=block.qubit_support,
+                    check_unitary=False,
+                    check_hermitian=True,
+                ),
                 qubit_support=self.qubit_support,
                 use_full_support=False,
             )

qadence/blocks/matrix.py

@@ -60,7 +60,13 @@ class MatrixBlock(PrimitiveBlock):
     name = "MatrixBlock"
     matrix: torch.Tensor
 
-    def __init__(self, matrix: torch.Tensor | np.ndarray, qubit_support: tuple[int, ...]) -> None:
+    def __init__(
+        self,
+        matrix: torch.Tensor | np.ndarray,
+        qubit_support: tuple[int, ...],
+        check_unitary: bool = True,
+        check_hermitian: bool = False,
+    ) -> None:
         if isinstance(matrix, np.ndarray):
             matrix = torch.tensor(matrix)
         if matrix.ndim == 3 and matrix.size(0) == 1:
@@ -69,10 +75,12 @@ def __init__(self, matrix: torch.Tensor | np.ndarray, qubit_support: tuple[int,
             raise TypeError("Please provide a 2D matrix.")
         if not self.is_square(matrix):
             raise TypeError("Please provide a square matrix.")
-        if not self.is_hermitian(matrix):
-            logger.warning("Provided matrix is not hermitian.")
-        if not self.is_unitary(matrix):
-            logger.warning("Provided matrix is not unitary.")
+        if check_hermitian:
+            if not self.is_hermitian(matrix):
+                logger.warning("Provided matrix is not hermitian.")
+        if check_unitary:
+            if not self.is_unitary(matrix):
+                logger.warning("Provided matrix is not unitary.")
         self.matrix = matrix.clone()
         super().__init__(qubit_support)
 

qadence/ml_tools/train_grad.py

@@ -282,6 +282,8 @@ def loss_fn(model: torch.nn.Module, data: torch.Tensor) -> tuple[torch.Tensor, d
             xs = next(dl_iter) if dataloader is not None else None  # type: ignore[arg-type]
             xs_to_device = data_to_device(xs, device=device, dtype=data_dtype)
             loss, metrics, *_ = loss_fn(model, xs_to_device)
+            if dataloader is None:
+                loss = loss.item()
             if iteration % config.print_every == 0 and config.verbose:
                 print_metrics(loss, metrics, iteration)