Merge branch 'refs/heads/l4_dev' into mem-problems-fix

# Conflicts:
#	cpp_test/TestLsd.cpp
#	src_cpp/lsd.hpp

.gitignore

@@ -1,4 +1,9 @@
 zscratch*
+<<<<<<< Updated upstream
+paper_lsd_data_collection/
+=======
+paper_lsd_data_collection/*
+>>>>>>> Stashed changes
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -350,3 +355,6 @@ build
 *.exe
 *.out
 *.app
+
+
+.DS_Store

CMakeLists.txt

@@ -13,7 +13,7 @@ project(main
 set(CMAKE_CXX_STANDARD 20)
 
 # Enable OpenMP support for parallel programming
-SET(CMAKE_CXX_FLAGS  "-fopenmp")
+# SET(CMAKE_CXX_FLAGS  "-fopenmp")
 
 # Ensure that the necessary C++11 features are available, as required by the RapidCSV library
 set(CMAKE_CXX_STANDARD_REQUIRED ON)

examples/sinter_example_owd.py

@@ -0,0 +1,197 @@
+import numpy as np
+import scipy.sparse
+import sinter
+import stim
+from matplotlib import pyplot as plt
+
+from ldpc.ckt_noise.sinter_overlapping_window_decoder import (
+    SinterDecoder_BPOSD_OWD,
+    SinterDecoder_LSD_OWD,
+    SinterDecoder_PyMatching_OWD,
+)
+
+from src_python.ldpc.ckt_noise.not_an_arb_ckt_simulator import stim_circuit_from_time_steps, get_stabilizer_time_steps
+
+
+def generate_decoders(ds: np.ndarray, decodings: np.ndarray):
+    decoders = {}
+    for d in ds:
+        for r in decodings:
+            decoders[f"bposd_owd_d{d}_r{r}"] = SinterDecoder_BPOSD_OWD(
+                decodings=int(r),
+                window=int(2 * d),
+                commit=int(d),
+                num_checks=int(d - 1),
+            )
+            decoders[f"lsd_owd_d{d}_r{r}"] = SinterDecoder_LSD_OWD(
+                decodings=int(r),
+                window=int(2 * d),
+                commit=int(d),
+                num_checks=int(d - 1),
+            )
+            decoders[f"pymatching_owd_d{d}_r{r}"] = SinterDecoder_PyMatching_OWD(
+                decodings=int(r),
+                window=int(2 * d),
+                commit=int(d),
+                num_checks=int((d**2 - 1)),
+                decoder_config={},
+            )
+
+            # decoders[f"pymatching_owd_d{d}_r{r}"] = SinterDecoder_PyMatching_OWD(
+            #     decodings=int(r),
+            #     window=int(2 * d),
+            #     commit=int(d),
+            #     num_checks=int(d - 1),
+            # )
+    return decoders
+
+def generate_example_tasks_surface(ps: np.ndarray, ds: np.ndarray, decodings: np.ndarray):
+    for r in decodings:
+        for p in ps:
+            for d in ds:
+                rounds = int((r + 1) * d)
+                sc_circuit = stim.Circuit.generated(
+                    rounds=rounds,
+                    distance=int(d),
+                    after_clifford_depolarization=p,
+                    after_reset_flip_probability=p,
+                    before_measure_flip_probability=p,
+                    before_round_data_depolarization=p,
+                    code_task=f"surface_code:rotated_memory_x",
+                )
+                yield sinter.Task(
+                    circuit=sc_circuit,
+                    decoder=f"pymatching_owd_d{d}_r{r}",
+                    json_metadata={
+                        "p": p,
+                        "d": int(d),
+                        # "decodings": int(rounds),
+                        # "commit": d,
+                        # "window": 2 * d,
+                        "decodings": int(r),
+                    },
+                )
+def generate_example_tasks_qera(ps: np.ndarray, ds: np.ndarray, decodings: np.ndarray):
+    for r in decodings:
+        for p in ps:
+            for d in ds:
+                pcm = scipy.sparse.load_npz(f"/home/luca/Documents/codeRepos/ldpc/python_test/pcms/hqcodes/HQ_{d}_hx.npz")
+                logicals = scipy.sparse.load_npz(f"/home/luca/Documents/codeRepos/ldpc/python_test/pcms/hqcodes/HQ_{d}_lx.npz")
+                time_steps, measured_bits = get_stabilizer_time_steps(pcm)
+
+                sc_circuit= stim_circuit_from_time_steps(
+                    pcm,
+                    logicals,
+                    time_steps,
+                    measured_bits,
+                    before_round_data_depolarization=p,
+                    after_clifford_depolarization=p,
+                    after_reset_flip_probability=p,
+                    before_measure_flip_probability=p,
+                    rounds=d,
+                )
+                yield sinter.Task(
+                    circuit=sc_circuit,
+                    decoder=f"lsd_owd_d{d}_r{r}",
+                    json_metadata={
+                        "p": p,
+                        "d": int(d),
+                        # "decodings": int(rounds),
+                        # "commit": d,
+                        # "window": 2 * d,
+                        "decodings": int(r),
+                    },
+                )
+
+
+def generate_example_tasks(ps: np.ndarray, ds: np.ndarray, decodings: np.ndarray):
+    for r in decodings:
+        for p in ps:
+            for d in ds:
+                rounds = int((r + 1) * d)
+                sc_circuit = stim.Circuit.generated(
+                    rounds=rounds,
+                    distance=int(d),
+                    after_clifford_depolarization=p,
+                    after_reset_flip_probability=p,
+                    before_measure_flip_probability=p,
+                    before_round_data_depolarization=p,
+                    code_task=f"surface_code:rotated_memory_x",
+                )
+                yield sinter.Task(
+                    circuit=sc_circuit,
+                    decoder=f"pymatching_owd_d{d}_r{r}",
+                    json_metadata={
+                        "p": p,
+                        "d": int(d),
+                        # "decodings": int(rounds),
+                        # "commit": d,
+                        # "window": 2 * d,
+                        "decodings": int(r),
+                    },
+                )
+
+
+def main():
+    decodings = np.array([2, 3])
+    ps = np.geomspace(2e-3, 0.011, 9)
+    ds = np.array([12])
+    samples = sinter.collect(
+        num_workers=6,
+        max_shots=50_000,
+        max_errors=500,
+        tasks=generate_example_tasks_qera(ps, ds, decodings),
+        custom_decoders=generate_decoders(ds, decodings),
+        print_progress=True,
+        save_resume_filepath=f"owd_sc.csv",
+    )
+
+    # Print samples as CSV data.
+    print(sinter.CSV_HEADER)
+    for sample in samples:
+        print(sample.to_csv_line())
+
+    # Render a matplotlib plot of the data.
+    fig, axis = plt.subplots(1, 3, sharey=True, figsize=(12, 4))
+    sinter.plot_error_rate(
+        ax=axis[0],
+        stats=samples,
+        group_func=lambda stat: f"d={stat.json_metadata['d']}",
+        filter_func=lambda stat: stat.json_metadata["decodings"] == 1,
+        x_func=lambda stat: stat.json_metadata["p"],
+    )
+
+    sinter.plot_error_rate(
+        ax=axis[1],
+        stats=samples,
+        group_func=lambda stat: f"d={stat.json_metadata['d']}",
+        filter_func=lambda stat: stat.json_metadata["decodings"] == 2,
+        x_func=lambda stat: stat.json_metadata["p"],
+    )
+
+    sinter.plot_error_rate(
+        ax=axis[2],
+        stats=samples,
+        group_func=lambda stat: f"d={stat.json_metadata['d']}",
+        filter_func=lambda stat: stat.json_metadata["decodings"] == 3,
+        x_func=lambda stat: stat.json_metadata["p"],
+    )
+
+    axis[0].set_ylabel("Logical Error Rate")
+    axis[0].set_title("Decodings = 1")
+    axis[1].set_title("Decodings = 2")
+    axis[2].set_title("Decodings = 3")
+    for ax in axis:
+        ax.loglog()
+        ax.grid()
+        ax.set_xlabel("Physical Error Rate")
+        ax.legend()
+
+    # Save to file and also open in a window.
+    fig.savefig("plot.png")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
+    # pass