Loss balancing

perceval/runtime/error_mitigation/__init__.py

@@ -27,6 +27,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from .loss_mitigation import photon_recycling, PhotonRecycling
+from .photon_recycling import photon_recycling, PhotonRecycling
 from .abstract_mitigation import AbstractMitigation
 from .compilation_averaging import CompilationAveraging
+from .loss_balancing import LossBalancing

perceval/runtime/error_mitigation/loss_balancing.py

@@ -0,0 +1,90 @@
+# MIT License
+#
+# Copyright (c) 2022 Quandela
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# As a special exception, the copyright holders of exqalibur library give you
+# permission to combine exqalibur with code included in the standard release of
+# Perceval under the MIT license (or modified versions of such code). You may
+# copy and distribute such a combined system following the terms of the MIT
+# license for both exqalibur and Perceval. This exception for the usage of
+# exqalibur is limited to the python bindings used by Perceval.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+from copy import copy
+from math import prod
+
+from perceval.utils import BSDistribution, ConversionHelper, NoiseModel
+from perceval.utils.constants import KEY_RESULTS
+
+from ..computation import Computation
+from .abstract_mitigation import AbstractMitigation
+
+
+class LossBalancing(AbstractMitigation):
+    """
+    Output loss balancing
+
+    Correct biasing in results due to non-uniform loss at the output of the PIC.
+    """
+
+    APPLY_MIN_PHOTONS = False
+    APPLY_LOGICAL_SELECTION = False
+
+    def extend_computation(
+        self,
+        computation: Computation,
+        noise: NoiseModel
+    ) -> list[Computation]:
+        return [computation]
+
+    def _parse_results(
+        self,
+        computation: Computation,
+        results: list[dict],
+        noise: NoiseModel
+    ) -> dict:
+        res = copy(results[0])
+        ratios = ... # TODO: Get the loss ratios data from QPU
+
+        assert all(ratio > 0 for ratio in ratios), (
+            "Bad calibration has led to invalid output transmittances."
+        )
+
+        dist = ConversionHelper.convert_to(
+            "probs",
+            res[KEY_RESULTS],
+            **computation.parameters,
+        )
+
+        # LossBalancing should be applied before the results are de-compiled
+        assert len(ratios) == dist.m, "Loss ratios do not match the distribution lengths."
+
+        balanced = BSDistribution()
+        for state, prob in dist.items():
+            balanced.add(
+                state, prob * prod(
+                    (1 / ratios[i]) ** n for i, n in enumerate(state)
+                ),
+            )
+
+        if len(balanced):
+            balanced.normalize()
+        res[KEY_RESULTS] = balanced
+        return res

perceval/runtime/error_mitigation/loss_mitigation.py → perceval/runtime/error_mitigation/photon_recycling.py

@@ -39,7 +39,7 @@
 
 from ..computation import Computation
 from .abstract_mitigation import AbstractMitigation
-from ._loss_mitigation_utils import _gen_lossy_dists, _get_avg_exp_from_uni_dist, _generate_one_photon_per_mode_mapping
+from .utils._photon_recycling import _gen_lossy_dists, _get_avg_exp_from_uni_dist, _generate_one_photon_per_mode_mapping
 
 
 def _validate_noisy_input(noisy_input: BSCount | BSDistribution, ideal_photon_count: int):