rm tensor inputs in apply_counts

pyqtorch/noise/readout.py

@@ -7,7 +7,7 @@
 from torch import Tensor
 from torch.distributions import normal, poisson, uniform
 
-from pyqtorch.utils import OrderedCounter, counts_to_orderedcounter
+from pyqtorch.utils import OrderedCounter
 
 
 class WhiteNoise(Enum):
@@ -271,36 +271,22 @@ def apply_on_probas(self, batch_probs: Tensor, n_shots: int = 1000) -> Tensor:
         return output_probs
 
     def apply_on_counts(
-        self, counters: list[Counter | OrderedCounter] | Tensor, n_shots: int = 1000
-    ) -> list[Counter] | Tensor:
-        """Apply readout on counters represented as Counters or Tensors.
+        self, counters: list[Counter | OrderedCounter], n_shots: int = 1000
+    ) -> list[Counter]:
+        """Apply readout on counters represented as Counters.
 
         Args:
-            counters (list[Counter  |  OrderedCounter] | Tensor): Samples of bit string as Counters.
+            counters (list[Counter  |  OrderedCounter]): Samples of bit string as Counters.
             n_shots (int, optional): Number of shots to sample. Defaults to 1000.
 
         Returns:
-            list[Counter] | Tensor: Samples of corrupted bit strings
+            list[Counter]: Samples of corrupted bit strings
         """
         noise_matrix = self.create_noise_matrix(n_shots, False)
         err_idx = torch.as_tensor(noise_matrix < self.error_probability)  # type: ignore[operator]
 
         corrupted_bitstrings = []
-        if isinstance(counters, Tensor):
-            for bincount in counters:
-                counter = counts_to_orderedcounter(bincount, self.n_qubits)
-                sample = sample_to_matrix(counter)
-                corrupted_counter = bs_corruption(err_idx=err_idx, sample=sample)
-                corrupted_bincount = torch.zeros_like(bincount)
-                for bitstring, count in corrupted_counter.items():
-                    idx = int(bitstring, 2)
-                    corrupted_bincount[idx] = count
-                corrupted_bitstrings.append(corrupted_bincount)
-            corrupted_bitstrings = torch.stack(corrupted_bitstrings)
-        else:
-            for counter in counters:  # type: ignore[assignment]
-                sample = sample_to_matrix(counter)
-                corrupted_bitstrings.append(
-                    bs_corruption(err_idx=err_idx, sample=sample)
-                )
+        for counter in counters:  # type: ignore[assignment]
+            sample = sample_to_matrix(counter)
+            corrupted_bitstrings.append(bs_corruption(err_idx=err_idx, sample=sample))
         return corrupted_bitstrings