Review stabilizer reset option

setup.cfg

@@ -23,6 +23,7 @@ install_requires =
     h5py
     scikit-learn
     multipledispatch
+    pymatching
     cma
     qcecircuits  @ git+https://github.com/MiniSean/QCoCircuits.git  # QCoCircuits
 # Required additional data files

src/qce_interp/data_manager.py

@@ -239,8 +239,8 @@ def from_file_path(cls, file_path: Path, qec_rounds: List[int], heralded_initial
 
         for qubit_id in tqdm(involved_qubit_ids, desc='Processing data file'):
             channel_identifier: IAcquisitionChannelIdentifier = channel_identifier_lookup[qubit_id]
-            raw_shots: NDArray[np.float64] = data_dict[cls.data_key()][:, channel_identifier.channel_indices]
-            raw_complex_shots: NDArray[np.complex128] = StateAcquisitionContainer.real_imag_to_complex(raw_shots)
+            raw_shots: NDArray[np.float32] = data_dict[cls.data_key()][:, channel_identifier.channel_indices]
+            raw_complex_shots: NDArray[np.complex64] = StateAcquisitionContainer.real_imag_to_complex(raw_shots)
 
             # Qutrit calibration points
             calibration_points: StateAcquisitionContainer = StateAcquisitionContainer.from_state_acquisitions(

src/qce_interp/decoder_examples/lookup_table.py

@@ -286,6 +286,13 @@ def get_fidelity(self, cycle_stabilizer_count: int, target_state: np.ndarray) ->
             cycle_stabilizer_count=cycle_stabilizer_count,
             target_state=target_state,
         )
+
+    def get_fidelity_uncertainty(self, cycle_stabilizer_count: int, target_state: np.ndarray) -> float:
+        """:return: Uncertainty in Logical fidelity based on target state and stabilizer round-count."""
+        return self._syndrome_decoder.get_fidelity_uncertainty(
+            cycle_stabilizer_count=cycle_stabilizer_count,
+            target_state=target_state,
+        )
     # endregion
 
 
@@ -354,3 +361,7 @@ def binary_syndrome_lookup(self) -> Dict[tuple, tuple]:
         return result
     # endregion
 
+    # region Interface Methods
+    def get_fidelity_uncertainty(self, cycle_stabilizer_count: int, target_state: np.ndarray) -> float:
+        raise NotImplemented
+    # endregion

src/qce_interp/decoder_examples/majority_voting.py

@@ -51,5 +51,16 @@ def get_fidelity(self, cycle_stabilizer_count: int, target_state: np.ndarray) ->
                 counter += 1
         equal_fraction = counter / len(corrected_binary_output)
         return equal_fraction
+
+    def get_fidelity_uncertainty(self, cycle_stabilizer_count: int, target_state: np.ndarray) -> float:
+        """:return: Uncertainty in Logical fidelity based on target state and stabilizer round-count."""
+        logical_fidelity: float = self.get_fidelity(
+            cycle_stabilizer_count=cycle_stabilizer_count,
+            target_state=target_state,
+        )
+        # (N, 1, D)
+        binary_projected_classification: np.ndarray = self._error_identifier.get_binary_projected_classification(cycle_stabilizer_count=cycle_stabilizer_count)
+        number_of_samples: int = binary_projected_classification.shape[0]
+        return float(np.sqrt(logical_fidelity * (1.0 - logical_fidelity) / number_of_samples))
     # endregion
 

src/qce_interp/decoder_examples/mwpm_decoders.py

@@ -227,6 +227,17 @@ def get_fidelity(self, cycle_stabilizer_count: int, target_state: np.ndarray) ->
         equal_rows_count = np.sum(np.all(logical_error == 0, axis=1))
         equal_fraction: float = equal_rows_count / len(logical_error)
         return equal_fraction
+
+    def get_fidelity_uncertainty(self, cycle_stabilizer_count: int, target_state: np.ndarray) -> float:
+        """:return: Uncertainty in Logical fidelity based on target state and stabilizer round-count."""
+        logical_fidelity: float = self.get_fidelity(
+            cycle_stabilizer_count=cycle_stabilizer_count,
+            target_state=target_state,
+        )
+        # (N, 1, D)
+        binary_projected_classification: np.ndarray = self._error_identifier.get_binary_projected_classification(cycle_stabilizer_count=cycle_stabilizer_count)
+        number_of_samples: int = binary_projected_classification.shape[0]
+        return float(np.sqrt(logical_fidelity * (1.0 - logical_fidelity) / number_of_samples))
     # endregion
 
     # region Class Methods
@@ -429,6 +440,17 @@ def get_fidelity(self, cycle_stabilizer_count: int, target_state: np.ndarray = N
         error_rate = error_rate if self.initial_state == 0 else 1 - error_rate
 
         return 1 - error_rate
+
+    def get_fidelity_uncertainty(self, cycle_stabilizer_count: int, target_state: np.ndarray) -> float:
+        """:return: Uncertainty in Logical fidelity based on target state and stabilizer round-count."""
+        logical_fidelity: float = self.get_fidelity(
+            cycle_stabilizer_count=cycle_stabilizer_count,
+            target_state=target_state,
+        )
+        # (N, 1, D)
+        binary_projected_classification: np.ndarray = self._error_identifier.get_binary_projected_classification(cycle_stabilizer_count=cycle_stabilizer_count)
+        number_of_samples: int = binary_projected_classification.shape[0]
+        return float(np.sqrt(logical_fidelity * (1.0 - logical_fidelity) / number_of_samples))
     # endregion
 
     # region Class Methods