Handle boundary edge components and refactor iter_dem_instructions_include_correlations (#171)

oscarhiggott · noajshu · web-flow · commit 6c33cdb75318 · 2025-09-25T11:40:21.000-07:00
* Add more exhaustive tests of correlations. Fix a bug where edge weight rewrites in the search graph weren't always undone. * handle boundary edge component properly (fixing issue #175) and refactor * Add argument to determine if components are included as edges. Fix choice to include components. * remove support for cp38-macosx_arm64 * Add test to check for correlated matching boundary component handling (#176) Co-authored-by: oscarhiggott <29460323+oscarhiggott@users.noreply.github.com> --------- Co-authored-by: Noah Shutty <noajshu@users.noreply.github.com>
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -51,7 +51,7 @@ jobs:
           {os: macos-latest, dist: cp312-macosx_x86_64},
           {os: macos-latest, dist: cp313-macosx_x86_64},
           # macosx arm64
-          {os: macos-latest, dist: cp38-macosx_arm64},
+          # {os: macos-latest, dist: cp38-macosx_arm64},
           {os: macos-latest, dist: cp39-macosx_arm64},
           {os: macos-latest, dist: cp310-macosx_arm64},
           {os: macos-latest, dist: cp311-macosx_arm64},
diff --git a/src/pymatching/sparse_blossom/driver/user_graph.cc b/src/pymatching/sparse_blossom/driver/user_graph.cc
@@ -17,6 +17,19 @@
 #include "pymatching/rand/rand_gen.h"
 #include "pymatching/sparse_blossom/driver/implied_weights.h"
 
+namespace {
+
+double bernoulli_xor(double p1, double p2) {
+    return p1 * (1 - p2) + p2 * (1 - p1);
+}
+
+}  // namespace
+
+
+double pm::to_weight_for_correlations(double probability) {
+    return std::log((1 - probability) / probability);
+}
+
 double pm::merge_weights(double a, double b) {
     auto sgn = std::copysign(1, a) * std::copysign(1, b);
     auto signed_min = sgn * std::min(std::abs(a), std::abs(b));
@@ -342,6 +355,15 @@ void pm::UserGraph::handle_dem_instruction(
     }
 }
 
+void pm::UserGraph::handle_dem_instruction_include_correlations(
+    double p, const std::vector<size_t>& detectors, const std::vector<size_t>& observables) {
+    if (detectors.size() == 2) {
+        add_or_merge_edge(detectors[0], detectors[1], observables, pm::to_weight_for_correlations(p), p, INDEPENDENT);
+    } else if (detectors.size() == 1) {
+        add_or_merge_boundary_edge(detectors[0], observables, pm::to_weight_for_correlations(p), p, INDEPENDENT);
+    }
+}
+
 void pm::UserGraph::get_nodes_on_shortest_path_from_source(size_t src, size_t dst, std::vector<size_t>& out_nodes) {
     auto& mwpm = get_mwpm_with_search_graph();
     bool src_is_boundary = is_boundary_node(src);
@@ -444,18 +466,6 @@ double pm::UserGraph::get_edge_weight_normalising_constant(size_t max_num_distin
     }
 }
 
-namespace {
-
-double bernoulli_xor(double p1, double p2) {
-    return p1 * (1 - p2) + p2 * (1 - p1);
-}
-
-double to_weight(double probability) {
-    return std::log((1 - probability) / probability);
-}
-
-}  // namespace
-
 void pm::add_decomposed_error_to_joint_probabilities(
     DecomposedDemError& error,
     std::map<std::pair<size_t, size_t>, std::map<std::pair<size_t, size_t>, double>>& joint_probabilites) {
@@ -490,7 +500,7 @@ pm::UserGraph pm::detector_error_model_to_user_graph(
         pm::iter_dem_instructions_include_correlations(
             detector_error_model,
             [&](double p, const std::vector<size_t>& detectors, std::vector<size_t>& observables) {
-                user_graph.handle_dem_instruction(p, detectors, observables);
+                user_graph.handle_dem_instruction_include_correlations(p, detectors, observables);
             },
             joint_probabilites);
 
@@ -526,7 +536,7 @@ void pm::UserGraph::populate_implied_edge_weights(
                     // minimum of 0.5 as an implied probability for an edge to be reweighted.
                     double implied_probability_for_other_edge =
                         std::min(0.5, affected_edge_and_probability.second / marginal_probability);
-                    double w = to_weight(implied_probability_for_other_edge);
+                    double w = pm::to_weight_for_correlations(implied_probability_for_other_edge);
                     ImpliedWeightUnconverted implied{affected_edge.first, affected_edge.second, w};
                     edge.implied_weights_for_other_edges.push_back(implied);
                 }
diff --git a/src/pymatching/sparse_blossom/driver/user_graph.h b/src/pymatching/sparse_blossom/driver/user_graph.h
@@ -120,6 +120,8 @@ class UserGraph {
     Mwpm& get_mwpm();
     Mwpm& get_mwpm_with_search_graph();
     void handle_dem_instruction(double p, const std::vector<size_t>& detectors, const std::vector<size_t>& observables);
+    void handle_dem_instruction_include_correlations(
+        double p, const std::vector<size_t>& detectors, const std::vector<size_t>& observables);
     void get_nodes_on_shortest_path_from_source(size_t src, size_t dst, std::vector<size_t>& out_nodes);
     void populate_implied_edge_weights(
         std::map<std::pair<size_t, size_t>, std::map<std::pair<size_t, size_t>, double>>& joint_probabilites);
@@ -131,6 +133,8 @@ class UserGraph {
     bool _all_edges_have_error_probabilities;
 };
 
+double to_weight_for_correlations(double probability);
+
 template <typename EdgeCallable, typename BoundaryEdgeCallable>
 inline double UserGraph::iter_discretized_edges(
     pm::weight_int num_distinct_weights,
@@ -263,7 +267,8 @@ template <typename Handler>
 void iter_dem_instructions_include_correlations(
     const stim::DetectorErrorModel& detector_error_model,
     const Handler& handle_dem_error,
-    std::map<std::pair<size_t, size_t>, std::map<std::pair<size_t, size_t>, double>>& joint_probabilites) {
+    std::map<std::pair<size_t, size_t>, std::map<std::pair<size_t, size_t>, double>>& joint_probabilites,
+    bool include_decomposed_error_components_in_edge_weights = true) {
     detector_error_model.iter_flatten_error_instructions([&](const stim::DemInstruction& instruction) {
         double p = instruction.arg_data[0];
         pm::DecomposedDemError decomposed_err;
@@ -311,35 +316,23 @@ void iter_dem_instructions_include_correlations(
                 component->observable_indices.push_back(target.val());
             } else if (target.is_separator()) {
                 instruction_contains_separator = true;
-                // If the previous error in the decomposition had 3 or more detectors, we throw an exception.
-                if (num_component_detectors > 2) {
-                    throw std::invalid_argument(
-                        "Encountered a decomposed error instruction with a hyperedge component (3 or more detectors). "
-                        "This is not supported.");
-                } else if (num_component_detectors == 0) {
+                // We cannot have num_component_detectors > 2 at this point, or we would have already thrown an
+                // exception
+                if (num_component_detectors == 0) {
                     throw std::invalid_argument(
                         "Encountered a decomposed error instruction with an undetectable component (0 detectors). "
                         "This is not supported.");
-                } else if (num_component_detectors > 0) {
-                    // If the previous error in the decomposition had 1 or 2 detectors, we handle it
-                    handle_dem_error(p, {component->node1, component->node2}, component->observable_indices);
-                    decomposed_err.components.push_back({});
-                    component = &decomposed_err.components.back();
-                    component->node1 = SIZE_MAX;
-                    component->node2 = SIZE_MAX;
-                    num_component_detectors = 0;
                 }
+                // The previous error in the decomposition must have 1 or 2 detectors
+                decomposed_err.components.push_back({});
+                component = &decomposed_err.components.back();
+                component->node1 = SIZE_MAX;
+                component->node2 = SIZE_MAX;
+                num_component_detectors = 0;
             }
         }
 
-        if (num_component_detectors > 2) {
-            // Undecomposed hyperedges are not supported
-            throw std::invalid_argument(
-                "Encountered an undecomposed error instruction with 3 or mode detectors. "
-                "This is not supported when using `enable_correlations=True`. "
-                "Did you forget to set `decompose_errors=True` when "
-                "converting the stim circuit to a detector error model?");
-        } else if (num_component_detectors == 0) {
+        if (num_component_detectors == 0) {
             if (instruction_contains_separator) {
                 throw std::invalid_argument(
                     "Encountered a decomposed error instruction with an undetectable component (0 detectors). "
@@ -348,12 +341,17 @@ void iter_dem_instructions_include_correlations(
                 // Ignore errors that are undetectable, provided they are not a component of a decomposed error
                 return;
             }
+        }
 
-        } else if (num_component_detectors > 0) {
-            if (component->node2 == SIZE_MAX) {
-                handle_dem_error(p, {component->node1}, component->observable_indices);
-            } else {
-                handle_dem_error(p, {component->node1, component->node2}, component->observable_indices);
+        // If include_decomposed_error_components_in_edge_weights is False, then only add the edge into 
+        // the graph if it is not a component in a decomposed error with more than one component
+        if (include_decomposed_error_components_in_edge_weights || decomposed_err.components.size() == 1) {
+            for (pm::UserEdge& component : decomposed_err.components) {
+                if (component.node2 == SIZE_MAX) {
+                    handle_dem_error(p, {component.node1}, component.observable_indices);
+                } else {
+                    handle_dem_error(p, {component.node1, component.node2}, component.observable_indices);
+                }
             }
         }
 
diff --git a/src/pymatching/sparse_blossom/driver/user_graph.test.cc b/src/pymatching/sparse_blossom/driver/user_graph.test.cc
@@ -384,7 +384,7 @@ TEST(IterDemInstructionsTest, CombinedComplexDem) {
     pm::iter_dem_instructions_include_correlations(dem, handler, joint_probabilities);
 
     ASSERT_EQ(handler.handled_errors.size(), 4);
-
+    
     std::vector<HandledError> expected = {
         {0.1, 0, SIZE_MAX, {}}, {0.2, 1, 2, {0}}, {0.4, 8, SIZE_MAX, {}}, {0.4, 9, SIZE_MAX, {1}}};
     EXPECT_EQ(handler.handled_errors, expected);
@@ -485,14 +485,6 @@ TEST(UserGraph, PopulateImpliedEdgeWeights) {
 
     graph.populate_implied_edge_weights(joint_probabilities);
 
-    auto to_weight = [](double p) {
-        if (p == 1.0)
-            return -std::numeric_limits<double>::infinity();
-        if (p == 0.0)
-            return std::numeric_limits<double>::infinity();
-        return std::log((1 - p) / p);
-    };
-
     auto it_01 = std::find_if(graph.edges.begin(), graph.edges.end(), [](const pm::UserEdge& edge) {
         return edge.node1 == 0 && edge.node2 == 1;
     });
@@ -503,7 +495,7 @@ TEST(UserGraph, PopulateImpliedEdgeWeights) {
     ASSERT_EQ(implied_01.node2, 3);
 
     double p_01 = 0.1 / 0.26;
-    double w_01 = to_weight(p_01);
+    double w_01 = pm::to_weight_for_correlations(p_01);
     ASSERT_EQ(implied_01.implied_weight, w_01);
 
     auto it_23 = std::find_if(graph.edges.begin(), graph.edges.end(), [](const pm::UserEdge& edge) {
@@ -515,7 +507,7 @@ TEST(UserGraph, PopulateImpliedEdgeWeights) {
     const auto& implied_23 = it_23->implied_weights_for_other_edges[0];
     ASSERT_EQ(implied_23.node1, 0);
     ASSERT_EQ(implied_23.node2, 1);
-    ASSERT_EQ(implied_23.implied_weight, 0);
+    ASSERT_NEAR(implied_23.implied_weight, 0.0, 0.00001);
 }
 
 TEST(UserGraph, ConvertImpliedWeights) {
diff --git a/tests/matching/decode_test.py b/tests/matching/decode_test.py
@@ -400,6 +400,26 @@ def test_decode_to_edges_with_correlations():
     assert np.array_equal(edges, expected_edges)
 
 
+def test_correlated_matching_handles_single_detector_components():
+    stim = pytest.importorskip("stim")
+    p = 0.1
+    circuit = stim.Circuit.generated(
+        code_task="surface_code:rotated_memory_x",
+        distance=5,
+        rounds=5,
+        before_round_data_depolarization=p,
+    )
+    circ_str = str(circuit).replace(
+        f"DEPOLARIZE1({p})", f"PAULI_CHANNEL_1(0, {p}, 0)"
+    )
+    noisy_circuit = stim.Circuit(circ_str)
+    dem = noisy_circuit.detector_error_model(
+        decompose_errors=True, approximate_disjoint_errors=True
+    )
+    m = Matching.from_detector_error_model(dem, enable_correlations=True)
+    assert m.num_detectors > 0
+
+
 def test_load_from_circuit_with_correlations():
     stim = pytest.importorskip("stim")
     circuit = stim.Circuit.generated(
