fix typo and disable LTO

README.md

@@ -19,7 +19,7 @@ A Search-Based Decoder for Quantum Error Correction.
 Tesseract is a Most Likely Error decoder designed for Low Density Parity Check (LDPC) quantum
 error-correcting codes. It applies pruning heuristics and manifold orientation techniques during a
 search over the error subsets to identify the most likely error configuration consistent with the
-observed syndrome. Tesseract archives significant speed improvements over traditional integer
+observed syndrome. Tesseract achieves significant speed improvements over traditional integer
 programming-based decoders while maintaining comparable accuracy at moderate physical error rates.
 
 We tested the Tesseract decoder for:
@@ -37,7 +37,8 @@ We tested the Tesseract decoder for:
 *   **Stim and DEM Support:** processes [Stim](https://github.com/quantumlib/stim) circuit files and
     [Detector Error Model
     (DEM)](https://github.com/quantumlib/Stim/blob/main/doc/file_format_dem_detector_error_model.md)
-    files with arbitrary error models.
+    files with arbitrary error models. Zero-probability error instructions are
+    automatically removed when a DEM is loaded.
 *   **Parallel Decoding:** uses multithreading to accelerate the decoding process, making it
     suitable for large-scale simulations.
 *   **Efficient Beam Search:** implements a [beam search](https://en.wikipedia.org/wiki/Beam_search)
@@ -68,6 +69,19 @@ Tesseract uses [Bazel](https://bazel.build/) as its build system. To build the d
 bazel build src:all
 ```
 
+## Running Tests
+
+Unit tests are executed with Bazel. Run the quick test suite using:
+```bash
+bazel test //src:all
+```
+By default the tests use reduced parameters and finish in under 30 seconds.
+To run a more exhaustive suite with additional shots and larger distances, set:
+```bash
+TESSERACT_LONG_TESTS=1 bazel test //src:all
+```
+
+
 ## Usage
 
 The file `tesseract_main.cc` provides the main entry point for Tesseract Decoder. It can decode

src/BUILD

@@ -27,7 +27,6 @@ OPT_COPTS = select({
     "//conditions:default": [
         "-Ofast",
         "-fno-fast-math",
-        "-flto",
         "-march=native",
     ],
     ":debug": ["-Og"],
@@ -42,7 +41,6 @@ OPT_LINKOPTS = select({
     "//conditions:default": [
         "-Ofast",
         "-fno-fast-math",
-        "-flto",
         "-march=native",
     ],
     ":debug": [],
@@ -141,6 +139,9 @@ cc_test(
 cc_test(
     name = "common_tests",
     srcs = ["common.test.cc"],
+    copts = OPT_COPTS,
+    linkopts = OPT_LINKOPTS,
+    linkstatic = True,
     deps = [
         ":libcommon",
         "@gtest",

src/common.cc

@@ -113,6 +113,29 @@ stim::DetectorErrorModel common::merge_identical_errors(
   return out_dem;
 }
 
+stim::DetectorErrorModel common::remove_zero_probability_errors(
+    const stim::DetectorErrorModel& dem) {
+  stim::DetectorErrorModel out_dem;
+  for (const stim::DemInstruction& instruction : dem.flattened().instructions) {
+    switch (instruction.type) {
+      case stim::DemInstructionType::DEM_SHIFT_DETECTORS:
+        assert(false && "unreachable");
+        break;
+      case stim::DemInstructionType::DEM_ERROR:
+        if (instruction.arg_data[0] > 0) {
+          out_dem.append_dem_instruction(instruction);
+        }
+        break;
+      case stim::DemInstructionType::DEM_DETECTOR:
+        out_dem.append_dem_instruction(instruction);
+        break;
+      default:
+        assert(false && "unreachable");
+    }
+  }
+  return out_dem;
+}
+
 stim::DetectorErrorModel common::dem_from_counts(
     stim::DetectorErrorModel& orig_dem, const std::vector<size_t>& error_counts,
     size_t num_shots) {
@@ -121,6 +144,17 @@ stim::DetectorErrorModel common::dem_from_counts(
         "Error hits array must be the same size as the number of errors in the "
         "original DEM.");
   }
+
+  for (const stim::DemInstruction& instruction :
+       orig_dem.flattened().instructions) {
+    if (instruction.type == stim::DemInstructionType::DEM_ERROR &&
+        instruction.arg_data[0] == 0) {
+      throw std::invalid_argument(
+          "dem_from_counts requires DEMs without zero-probability errors. Use"
+          " remove_zero_probability_errors first.");
+    }
+  }
+
   stim::DetectorErrorModel out_dem;
   size_t ei = 0;
   for (const stim::DemInstruction& instruction :
@@ -130,14 +164,11 @@ stim::DetectorErrorModel common::dem_from_counts(
         assert(false && "unreachable");
         break;
       case stim::DemInstructionType::DEM_ERROR: {
-        // Ignore zero-probability errors
-        if (instruction.arg_data[0] > 0) {
-          double est_probability =
-              double(error_counts.at(ei)) / double(num_shots);
-          out_dem.append_error_instruction(est_probability,
-                                           instruction.target_data, /*tag=*/"");
-          ++ei;
-        }
+        double est_probability =
+            double(error_counts.at(ei)) / double(num_shots);
+        out_dem.append_error_instruction(est_probability,
+                                         instruction.target_data, /*tag=*/"");
+        ++ei;
         break;
       }
       case stim::DemInstructionType::DEM_DETECTOR: {

src/common.h

@@ -71,8 +71,15 @@ struct Error {
 stim::DetectorErrorModel merge_identical_errors(
     const stim::DetectorErrorModel& dem);
 
+// Returns a copy of the given error model with any zero-probability DEM_ERROR
+// instructions removed.
+stim::DetectorErrorModel remove_zero_probability_errors(
+    const stim::DetectorErrorModel& dem);
+
 // Makes a new dem where the probabilities of errors are estimated from the
 // fraction of shots they were used in.
+// Throws std::invalid_argument if `orig_dem` contains zero-probability errors;
+// call remove_zero_probability_errors first.
 stim::DetectorErrorModel dem_from_counts(
     stim::DetectorErrorModel& orig_dem, const std::vector<size_t>& error_counts,
     size_t num_shots);

src/common.test.cc

@@ -25,3 +25,83 @@ TEST(common, ErrorsStructFromDemInstruction) {
   EXPECT_EQ(ES.symptom.detectors, std::vector<int>{1});
   EXPECT_EQ(ES.symptom.observables, 0b01);
 }
+
+TEST(common, DemFromCountsRejectsZeroProbabilityErrors) {
+  stim::DetectorErrorModel dem(R"DEM(
+        error(0.1) D0
+        error(0) D1
+        error(0.2) D2
+        detector(0, 0, 0) D0
+        detector(0, 0, 0) D1
+        detector(0, 0, 0) D2
+      )DEM");
+
+  std::vector<size_t> counts{1, 7, 4};
+  size_t num_shots = 10;
+  EXPECT_THROW({
+    common::dem_from_counts(dem, counts, num_shots);
+  }, std::invalid_argument);
+
+  stim::DetectorErrorModel cleaned = common::remove_zero_probability_errors(dem);
+  stim::DetectorErrorModel out_dem =
+      common::dem_from_counts(cleaned, std::vector<size_t>{1, 4}, num_shots);
+
+  auto flat = out_dem.flattened();
+  ASSERT_EQ(out_dem.count_errors(), 2);
+  ASSERT_GE(flat.instructions.size(), 2);
+
+  EXPECT_EQ(flat.instructions[0].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[0].arg_data[0], 0.1, 1e-9);
+  ASSERT_EQ(flat.instructions[1].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[1].arg_data[0], 0.4, 1e-9);
+}
+
+TEST(common, DemFromCountsSimpleTwoErrors) {
+  stim::DetectorErrorModel dem(R"DEM(
+        error(0.25) D0
+        error(0.35) D1
+        detector(0, 0, 0) D0
+        detector(0, 0, 0) D1
+      )DEM");
+
+  std::vector<size_t> counts{5, 7};
+  size_t num_shots = 20;
+  stim::DetectorErrorModel out_dem =
+      common::dem_from_counts(dem, counts, num_shots);
+
+  auto flat = out_dem.flattened();
+  ASSERT_EQ(out_dem.count_errors(), 2);
+
+  ASSERT_GE(flat.instructions.size(), 2);
+  EXPECT_EQ(flat.instructions[0].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[0].arg_data[0], 0.25, 1e-9);
+  EXPECT_EQ(flat.instructions[1].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[1].arg_data[0], 0.35, 1e-9);
+}
+
+TEST(common, RemoveZeroProbabilityErrors) {
+  stim::DetectorErrorModel dem(R"DEM(
+        error(0.1) D0
+        error(0) D1
+        error(0.2) D2
+        detector(0, 0, 0) D0
+        detector(0, 0, 0) D1
+        detector(0, 0, 0) D2
+      )DEM");
+
+  stim::DetectorErrorModel cleaned =
+      common::remove_zero_probability_errors(dem);
+
+  EXPECT_EQ(cleaned.count_errors(), 2);
+  auto flat = cleaned.flattened();
+  ASSERT_EQ(flat.instructions[0].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[0].arg_data[0], 0.1, 1e-9);
+  ASSERT_EQ(flat.instructions[1].type,
+            stim::DemInstructionType::DEM_ERROR);
+  EXPECT_NEAR(flat.instructions[1].arg_data[0], 0.2, 1e-9);
+}

src/simplex.cc

@@ -22,6 +22,7 @@
 constexpr size_t T_COORD = 2;
 
 SimplexDecoder::SimplexDecoder(SimplexConfig _config) : config(_config) {
+  config.dem = common::remove_zero_probability_errors(config.dem);
   std::vector<double> detector_t_coords(config.dem.count_detectors());
   for (const stim::DemInstruction& instruction :
        config.dem.flattened().instructions) {
@@ -30,10 +31,8 @@ SimplexDecoder::SimplexDecoder(SimplexConfig _config) : config(_config) {
         assert(false && "unreachable");
         break;
       case stim::DemInstructionType::DEM_ERROR: {
-        // Ignore zero-probability errors
-        if (instruction.arg_data[0] > 0) {
-          errors.emplace_back(instruction);
-        }
+        assert(instruction.arg_data[0] > 0);
+        errors.emplace_back(instruction);
         break;
       }
       case stim::DemInstructionType::DEM_DETECTOR:

src/simplex_main.cc

@@ -175,6 +175,7 @@ struct Args {
     if (!no_merge_errors) {
       config.dem = common::merge_identical_errors(config.dem);
     }
+    config.dem = common::remove_zero_probability_errors(config.dem);
 
     if (sample_num_shots > 0) {
       assert(!circuit_path.empty());

src/tesseract.cc

@@ -37,6 +37,7 @@ double TesseractDecoder::get_detcost(size_t d,
 }
 
 TesseractDecoder::TesseractDecoder(TesseractConfig config_) : config(config_) {
+  config.dem = common::remove_zero_probability_errors(config.dem);
   if (config.det_orders.empty()) {
     config.det_orders.emplace_back(config.dem.count_detectors());
     std::iota(config.det_orders[0].begin(), config.det_orders[0].end(), 0);

src/tesseract.perf.cc

@@ -77,6 +77,7 @@ void benchmark_tesseract(std::string circuit_path, size_t num_shots) {
           /*approximate_disjoint_errors_threshold=*/1,
           /*ignore_decomposition_failures=*/false,
           /*block_decomposition_from_introducing_remnant_edges=*/false);
+  dem = common::remove_zero_probability_errors(dem);
   TesseractConfig config{dem};
   config.det_beam = 20;
   config.pqlimit = 10'000'000;
@@ -99,6 +100,7 @@ void benchmark_simplex(std::string circuit_path, size_t num_shots) {
           /*approximate_disjoint_errors_threshold=*/1,
           /*ignore_decomposition_failures=*/false,
           /*block_decomposition_from_introducing_remnant_edges=*/false);
+  dem = common::remove_zero_probability_errors(dem);
   SimplexConfig config{dem};
   config.parallelize = true;
   SimplexDecoder decoder(config);

src/tesseract.test.cc

@@ -15,6 +15,7 @@
 #include "tesseract.h"
 
 #include <vector>
+#include <cstdlib>
 
 #include "gtest/gtest.h"
 #include "simplex.h"
@@ -79,10 +80,19 @@ bool simplex_test_compare(stim::DetectorErrorModel& dem,
 }
 
 TEST(tesseract, Tesseract_simplex_test) {
-  for (float p_err : {0.001, 0.003, 0.005}) {
-    for (size_t distance : {3, 5}) {
-      for (const size_t num_rounds : {2, 5, 10}) {
-        const size_t num_shots = 1000 / num_rounds / distance;
+  bool long_tests = std::getenv("TESSERACT_LONG_TESTS") != nullptr;
+  auto p_errs = long_tests ? std::vector<float>{0.001f, 0.003f, 0.005f}
+                           : std::vector<float>{0.003f};
+  auto distances = long_tests ? std::vector<size_t>{3, 5, 7}
+                              : std::vector<size_t>{3};
+  auto rounds = long_tests ? std::vector<size_t>{2, 5, 10}
+                           : std::vector<size_t>{2};
+  size_t base_shots = long_tests ? 1000 : 100;
+
+  for (float p_err : p_errs) {
+    for (size_t distance : distances) {
+      for (const size_t num_rounds : rounds) {
+        const size_t num_shots = base_shots / num_rounds / distance;
         std::cout << "p_err = " << p_err << " distance = " << distance
                   << " num_rounds = " << num_rounds
                   << " num_shots = " << num_shots << std::endl;
@@ -195,3 +205,24 @@ TEST(tesseract, Tesseract_simplex_DEM_exhaustive_test) {
     ASSERT_TRUE(return_val);
   }
 }
+
+TEST(tesseract, DecodersStripZeroProbabilityErrors) {
+  stim::DetectorErrorModel dem(R"DEM(
+        error(0.1) D0
+        error(0) D1
+        error(0.2) D2
+        detector(0,0,0) D0
+        detector(0,0,0) D1
+        detector(0,0,0) D2
+      )DEM");
+
+  TesseractConfig t_config{dem};
+  TesseractDecoder t_dec(t_config);
+  EXPECT_EQ(t_dec.config.dem.count_errors(), 2);
+  EXPECT_EQ(t_dec.errors.size(), 2);
+
+  SimplexConfig s_config{dem};
+  SimplexDecoder s_dec(s_config);
+  EXPECT_EQ(s_dec.config.dem.count_errors(), 2);
+  EXPECT_EQ(s_dec.errors.size(), 2);
+}

src/tesseract_main.cc

@@ -170,6 +170,7 @@ struct Args {
     if (!no_merge_errors) {
       config.dem = common::merge_identical_errors(config.dem);
     }
+    config.dem = common::remove_zero_probability_errors(config.dem);
 
     // Sample orientations of the error model to use for the det priority
     {