magic-state-cultivation/tools/write_historical_data_csv.py at main · Strilanc/magic-state-cultivation · GitHub

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#!/usr/bin/env python
import argparse
import csv
import json
import io
import pathlib
import sys

from typing import Any

import sinter
import stim

src_path = pathlib.Path(__file__).parent.parent / 'src'
assert src_path.exists()
sys.path.append(str(src_path))

import cultiv
import gen

# Rewritten (and extended) data starting from what was included in https://arxiv.org/abs/1905.06903

litinsky_data_csv = """
state,c,lbl,p_phys,p_out,qubits,cycles
T,2020 Litinski,"T15₇,₃,₃",1e-4,4.4e-8,810,18.1
T,2020 Litinski,"T15₉,₃,₃",1e-4,9.3e-10,1150,18.1
T,2020 Litinski,"T15₁₁,₅,₅",1e-4,1.9e-11,2070,30.0
T,2020 Litinski,"T15₉,₃,₃×T20₁₅,₇,₉",1e-4,2.4e-15,16400,90.3
T,2020 Litinski,"T15₉,₃,₃×T15₂₅,₉,₉",1e-4,6.3e-25,18600,67.8
T,2020 Litinski,"T15₁₇,₇,₇",1e-3,4.5e-8,4620,42.6
T,2020 Litinski,"T15₁₃,₅,₅×T20₂₃,₁₁,₁₃",1e-3,1.4e-10,43300,130
T,2020 Litinski,"T15₁₃,₅,₅×T20₂₇,₁₃,₁₅",1e-3,2.6e-11,46800,157
T,2020 Litinski,"T15₁₁,₅,₅×T15₂₅,₁₁,₁₁",1e-3,2.7e-12,30700,82.5
T,2020 Litinski,"T15₁₃,₅,₅×T15₂₉,₁₁,₁₃",1e-3,3.3e-14,39100,97.5
T,2020 Litinski,"T15₁₇,₇,₇×T15₄₁,₁₇,₁₇",1e-3,4.5e-20,73400,128
T,2020 Litinski,"T15₉,₃,₃",1e-4,1.5e-9,762,36.2
T,2020 Litinski,"T15₉,₅,₅×T15₂₁,₉,₁₁",1e-3,6.1e-10,7780,469
CCZ,2020 Litinski,"T15₇,₃,₃×CCZ8₁₅,₇,₉",1e-4,7.2e-14,12400,36.1
CCZ,2020 Litinski,"T15₁₃,₇,₇×CCZ8₂₅,₁₅,₁₅",1e-3,5.2e-11,47000,60.0
T,2019 Litinski,"T15",1e-4,3.5e-11,3720,143
CCZ,2018 Gidney et al,"T15×CCZ8",1e-3,6.3e-11,126400,171
2T,2018 Gidney et al,"T15→2T",1e-3,6.3e-11,126400,202
T,2018 Fowler et al,"T15×T15",1e-3,1.0e-14,177000,202
T,2018 Fowler et al,"T15",1e-3,3.5e-8,14400,120
T,2012 Fowler et al,"T15×T15",1e-3,3.0e-15,800000,250
T[d=3],2024 Itogawa et al,"T15",1e-3,1e-4,25,4
T,2024 Hirano et al,"T15",1e-3,1e-8,3e5,1
T,2024 Hirano et al,"T15",1e-3,1e-10,6e5,1
T,2024 Hirano et al,"T15",1e-3,2e-11,2e6,1
""".strip()

# Data from https://arxiv.org/abs/2302.12292
hook_data_csv = """
shots,errors,discards,seconds,decoder,strong_id,json_metadata
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  51956164,      1000,   6379976, 10435.3,internal_correlated,83bd3cda92815d378b45bb67bcd42c05dde6d2e5c539d0791899c9a4d003a0cb,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0001,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
  26035235,      1016,   3181744,  4343.0,internal_correlated,2eda7cc9c4c4c40e7222f2969ea7ef069a2d38ef7ffed564c4c150839417e708,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0002,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  28955612,      1006,   6674503,  4783.6,internal_correlated,9a402eeb62add7911b90a3176c55749ed6e63e92d1a04f63b82a887ac5b4bce8,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0002,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
  16350023,      1004,   2902698,  2837.7,internal_correlated,46ef37cad45cc1e05da4bfb313b0c2466c5888afdc2ec4c1258b3a66ddcb891a,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0003,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  21084041,      1011,   6848845,  3152.8,internal_correlated,df4fa1af27356ce8eb481b4f6b44d59346ed69cda777e0892a501bd4c3933ed5,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0003,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
   9870204,      1014,   2741603,  1569.3,internal_correlated,c8cdd354d8e002cad96f09202715bbb356e3ef4184b48239f3e300404045ecd9,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0005,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  15275741,      1001,   7336679,  1843.1,internal_correlated,1c6b90e7ebb29407385af0851571a3b47acb95e689e5065fc47cff2b808dd252,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0005,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
   7022678,      1011,   2571849,  1047.5,internal_correlated,19dd780c8a3f29753985a8df3561064e8fbfeb8a465af3552adb40e6a9d3142b,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0007,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  13353495,      1015,   8009857,  2036.6,internal_correlated,dc38fcdd3ad67a79c97ff9f34769c03c242345830f9f32b663375519709f468b,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0007,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
   4263786,      1004,   2038050,   794.7,internal_correlated,7d17d65098bd3ae188245aef29987da6a2a2079041cf4fd744b1bde607842455,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.001,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  11457471,      1035,   8354715,  1556.5,internal_correlated,78360d83316c4f3f4de721e4c32339bc7b30aca5add9b38778dc3769514283d4,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.001,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
   1434548,      1013,   1041817,   368.3,internal_correlated,daad49bcfc072011b9a2ccfc06db2fb3678ff0d9acfbdda854f41d9142801daf,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.002,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
  10518719,      1087,   9742266,   914.0,internal_correlated,90ff2fa42569ec73b4c80beb880a13e28160437955621e3085197b01e110b2af,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.002,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
    585930,      1001,    501546,   186.5,internal_correlated,4681ccca4ec2d5bc0fa5d8c9bd23404910cfeb3c0e49bcee4707f90cf4786d4f,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.003,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
   7055049,      1093,   6912754,   451.4,internal_correlated,1433fb06332256ba9ec8bc581865f6a431b36db22c7d243fb4f7a84a140c8505,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.003,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
    152279,      1014,    146064,    53.5,internal_correlated,e06437bee075e79333e69ae3e00f87e1946b1292e7ed3ff8dedcc770f422ab1d,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.005,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
   4783814,      1034,   4776479,   233.1,internal_correlated,45dff86556999e79a5378e5c42a3cf5d1bb314832fe6cab4c1bdb8d315f92b72,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.005,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
   1081601,      1016,   1079566,   108.4,internal_correlated,9791918d7e1fa1ae52e61d38671c653894bf2e3f54e3dfed2a07c91cfce9d9f7,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.01,""post_d"":5,""post_q"":49,""post_r"":2,""q"":449,""r"":15}"
 100000000,       153,  99999691,  5551.8,internal_correlated,f97279cc889b1e98cc8ba43af806c101dc32b247991f2ff13e419671831966e1,"{""b"":""hook_inject_X"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.01,""post_d"":7,""post_q"":97,""post_r"":2,""q"":449,""r"":15}"
  42890942,      1023,   2725432, 14104.6,internal_correlated,729aedebf140a3c102d13d31e0d51edfb1963608c73a1a0660c06aac265db4c5,"{""b"":""hook_inject_Y"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0001,""post_d"":5,""post_q"":49,""post_r"":2,""q"":463,""r"":15}"
  46089861,      1035,   5695822, 13345.7,internal_correlated,92379d7db08940eba1b49a1b7d6ba44b222d7e2cf83179cbb63d91c0c37a4913,"{""b"":""hook_inject_Y"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0001,""post_d"":7,""post_q"":97,""post_r"":2,""q"":463,""r"":15}"
  19285330,      1005,   2369229,  5403.0,internal_correlated,3b9ec8a5b72b8cca425fd056604cd122892519cf580fb89b0889fad491b979b2,"{""b"":""hook_inject_Y"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0002,""post_d"":5,""post_q"":49,""post_r"":2,""q"":463,""r"":15}"
  22089744,      1012,   5121860,  5803.9,internal_correlated,c9e28bdcd02f0fce9b25ec84f5789520b0325d90067c38a162931d7e878ca0a8,"{""b"":""hook_inject_Y"",""d"":15,""gates"":""cz"",""noise"":""SI1000"",""p"":0.0002,""post_d"":7,""post_q"":97,""post_r"":2,""q"":463,""r"":15}"
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""".strip()


# Data from https://arxiv.org/abs/2302.07395
y_data_csv = """
shots,errors,discards,seconds,decoder,strong_id,json_metadata
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""".strip()


# Data from table 1 of https://arxiv.org/abs/2409.07707
lee_et_al_2024_csv = """
                 Scheme, Output infidelity, Failure rate, Space cost, Time cost, Effective spacetime cost,   Tm,   Tintv, Tidle
"sng-(17,8,12,7)",                 1.72E-5,      5.37E-2,       2149,       512,                   1.16E6,     ,        ,
"sng-(23,16,14,9)",                1.13E-6,      2.52E-2,       3701,       640,                   2.43E6,     ,        ,
"sng-(29,20,18,11)",               1.27E-7,      1.82E-2,       5909,       768,                   4.62E6,     ,        ,
"sng-(47,24,26,23)",               3.58E-8,      1.51E-2,     1.31E4,      1536,                   2.04E7,     ,        ,
"cmb-(47,26,30,17,7,5)",           1.15E-8,      6.99E-3,     3.06E4,    2.28E5,                   7.03E9,    3,    3559,   693
"cmb-(53,38,34,19,7,5)",           1.04E-9,      4.21E-3,     3.99E4,    2.29E5,                   9.18E9,    3,    3579,   674
"cmb-(67,38,34,33,7,5)",          1.82E-10,      2.59E-3,     7.06E4,    2.26E5,                  1.60E10,    3,    3531,   638
""".strip()


def parse_field(f: Any) -> int | float | str:
    if isinstance(f, str):
        try:
            return int(f)
        except ValueError:
            try:
                return float(f)
            except ValueError:
                return f
    if isinstance(f, float):
        if f == int(f):
            return int(f)
    if isinstance(f, (int, float)):
        return f
    return str(f)


def select_rep_stats(stats: list[sinter.TaskStats], circuit: stim.Circuit) -> list[tuple[sinter.TaskStats, float]]:
    stats = [stat for stat in stats if stat.shots > stat.discards]
    vols = []
    errs = []
    baseline = cultiv.compute_expected_injection_growth_volume(
        circuit,
        discard_rate=0,
    )
    for stat in stats:
        keep_rate = (stat.shots - stat.discards) / stat.shots
        vols.append(baseline / keep_rate)
        errs.append(stat.errors / (stat.shots - stat.discards))
    indices = sorted(range(len(stats)), key=lambda e: (errs[e], vols[e]))
    vols = [vols[k] for k in indices]
    errs = [errs[k] for k in indices]
    stats = [stats[k] for k in indices]

    result = []
    prev_vol = None
    for k in range(len(stats)):
        if prev_vol is not None and vols[k] > prev_vol * 0.9 and errs[k] != 0:
            continue
        prev_vol = vols[k]
        result.append((stats[k], vols[k]))

    return result


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--in", nargs='+', type=str, default=())
    args = parser.parse_args()
    new_stats = sinter.read_stats_from_csv_files(*getattr(args, 'in'))
    print(sinter.CSV_HEADER)

    k = 0
    reader = csv.DictReader(io.StringIO(lee_et_al_2024_csv))
    reader.fieldnames = [e.strip() for e in reader.fieldnames]
    for item in reader:
        error_rate = float(item['Output infidelity'].strip())
        discard_rate = float(item['Failure rate'].strip())
        qubits = int(float(item['Space cost']))

        # > The time cost is quantified by the number of
        # > time steps required for each attempt of the scheme. Note
        # > that a single syndrome extraction round can be done in
        # > eight time steps by selecting the entangling gate schedule
        # > appropriate"
        TIME_ADJUSTMENT_FACTOR = 8
        rounds = int(float(item['Time cost'])) / TIME_ADJUSTMENT_FACTOR
        volume = float(item['Effective spacetime cost'].strip()) / TIME_ADJUSTMENT_FACTOR

        print(sinter.TaskStats(
            strong_id=f'''anon{k}''',
            decoder="estimated",
            json_metadata={
                'q': qubits,
                'r': rounds,
                'p': 1e-3,
                'noise': 'uniform',
                'c': '2024 Lee et al',
                'v': volume,
                'state': 'T',
            },
            shots=round(100 / error_rate / (1 - discard_rate)),
            errors=100,
            discards=round(100 / error_rate / (1 - discard_rate) * discard_rate),
        ))
        k += 1

    def s2p(stat: sinter.TaskStats) -> complex:
        q = stat.json_metadata['post_q']
        r = stat.json_metadata['post_r']
        assert r
        x = q * r * (stat.shots + 2) / (stat.shots - stat.discards + 1)
        y = stat.errors / (stat.shots - stat.discards)
        return x + 1j*y

    for group in sinter.group_by(sinter.read_stats_from_csv_files(io.StringIO(hook_data_csv)), key=lambda stat: stat.json_metadata['b']).values():
        points = [s2p(stat) for stat in group]
        for stat in group:
            p = s2p(stat)
            if any(q.real < p.real and q.imag < p.imag * 1.1 for q in points):
                continue
            row = stat.json_metadata
            if row['b'] == 'hook_inject_Y_magic_verify':
                c = '2023 Gidney'
            elif row['b'] == 'li_inject_Y_magic_verify':
                c = '2014 Li'
            elif row['b'] == 'zz_tweaked_inject_Y_magic_verify':
                c = '2022 Singh et al'
            else:
                continue
            print(sinter.TaskStats(
                strong_id=f'''anon{k}''',
                decoder="estimated",
                json_metadata={
                    'q': row['post_q'],
                    'r': row['post_r'],
                    'p': row['p'],
                    'noise': row['noise'],
                    'c': c,
                    'lbl': '',
                    'state': 'T',
                },
                shots=stat.shots,
                errors=stat.errors,
                discards=stat.discards,
            ))
            k += 1

    for row in csv.DictReader(io.StringIO(litinsky_data_csv)):
        row = {k: parse_field(v) for k, v in row.items()}
        print(sinter.TaskStats(
            strong_id=f'''anon{k}''',
            decoder="estimated",
            json_metadata={
                'q': row['qubits'],
                'r': row['cycles'] / (2 if row['state'] == '2T' else 1),
                'p': row['p_phys'],
                'noise': 'uniform',
                'c': row['c'],
                'lbl': row['lbl'],
                'state': 'T' if row['state'] == '2T' else row['state'],
            },
            shots=round(10 / row['p_out'] * (2 if row['state'] == '2T' else 1)),
            errors=10,
            discards=0,
        ))
        k += 1

    # Cryptography cost reference lines.
    # for v in [2, 5e8]:
    #     print(sinter.TaskStats(
    #         strong_id=f'''ref{k}''',
    #         decoder="estimated",
    #         json_metadata={'q': 1, 'r': v, 'p': 1e-3, 'noise': 'SI1000', 'c': f'''Sufficient to break RSA2048 (Gidney et al 2019)''', 'state': 'CCZ'},
    #         shots=100000000000,
    #         errors=round(100000000000/2.7e9),
    #         discards=0,
    #     ))
    #     k += 1
    # for v in [2, 5e8]:
    #     print(sinter.TaskStats(
    #         strong_id=f'''ref{k}''',
    #         decoder="estimated",
    #         json_metadata={'q': 1, 'r': v, 'p': 1e-3, 'noise': 'SI1000', 'c': f'''Sufficient to break ECDLP256 (Häner et al 2020)''', 'state': 'T'},
    #         shots=100000000000,
    #         errors=round(100000000000/(2**34 * 1.4)),
    #         discards=0,
    #     ))
    #     k += 1

    # Inplace Y reference (actual data).
    for row in csv.DictReader(io.StringIO(y_data_csv)):
        row = {k: parse_field(v) for k, v in row.items()}
        row.update(json.loads(row['json_metadata']))
        if row['b'] == 'Y' and row['p'] == 1e-3 and row['rb'] == row['d'] // 2 and row['r'] == row['d']:
            print(sinter.TaskStats(
                strong_id=f'''ref{k}''',
                decoder=row['decoder'],
                json_metadata={
                    'q': row['q'],
                    'r': row['rb'] + 1,
                    'p': row['p'],
                    'noise': row['noise'],
                    'c': f'''Preparing S|+⟩, for comparison''',
                    'lbl': '',
                    'state': 'Y',
                },
                shots=row['shots'] * 2,  # *2 to counter double-counting errors due to RY and MY both being in the experiment
                errors=row['errors'],
                discards=row['discards'],
            ))
        else:
            continue
        k += 1
    # Inplace Y reference (extrapolated).
    k += 1
    for v in range(19, 50, 2):
        print(sinter.TaskStats(
            strong_id=f'''ref{k}''',
            decoder="projected",
            json_metadata={
                'q': v * v * 2 + v - 1,
                'r': v // 2 + 1,
                'p': 1e-3,
                'noise': 'SI1000',
                'c': f'''Preparing S|+⟩, for comparison''',
                'state': 'Y',
                'projected': 1,
            },
            shots=round(2.5**v * 13),
            errors=1,
            discards=0,
        ))
        k += 1

    DOUBLING_S_GIVES_T_ASSUMPTION = 2
    match_stats = [
        stat
        for stat in new_stats
        if stat.decoder == 'perfectionist'
        if stat.json_metadata.get('d1') == 3
        if stat.json_metadata.get('c') == 'inject[unitary]+cultivate'
        if stat.json_metadata.get('p') == 1e-3
        if stat.json_metadata.get('noise') == 'uniform'
    ]
    if len(match_stats) == 1:
        stat, = match_stats
        print(sinter.TaskStats(
            strong_id=f'''ref{k}''',
            json_metadata={**stat.json_metadata, 'c': f'''2024 This Work (d1=3)''', 'state': 'T[d=3]'},
            decoder=stat.decoder,
            shots=stat.shots,
            errors=stat.errors * DOUBLING_S_GIVES_T_ASSUMPTION,
            discards=stat.discards,
        ))
        k += 1
    else:
        print(f"WARNING: found {len(match_stats)} instead of one d1=3 c=inject[unitary]+cultivate p=1e-3 for historical data plot", file=sys.stderr)

    match_stats = [
        stat
        for stat in new_stats
        if stat.decoder == 'perfectionist'
        if stat.json_metadata.get('d1') == 5
        if stat.json_metadata.get('c') == 'inject[unitary]+cultivate'
        if stat.json_metadata.get('p') == 1e-3
        if stat.json_metadata.get('noise') == 'uniform'
    ]
    if len(match_stats) == 1:
        stat, = match_stats
        print(sinter.TaskStats(
            strong_id=f'''ref{k}''',
            json_metadata={**stat.json_metadata, 'c': f'''2024 This Work (d1=5)''', 'state': 'T[d=5]'},
            decoder=stat.decoder,
            shots=stat.shots,
            errors=stat.errors * DOUBLING_S_GIVES_T_ASSUMPTION,
            discards=stat.discards,
        ))
        k += 1
    else:
        print("WARNING: didn't find d1=5 c=unitary p=1e-3 for historical data plot", file=sys.stderr)

    for p in [5e-4, 1e-3, 2e-3]:
        c3 = cultiv.make_end2end_cultivation_circuit(dcolor=3, dsurface=15, basis='Y', r_growing=3, r_end=1,
                                                     inject_style='unitary')
        match_stats = [
            stat
            for stat in new_stats
            if stat.decoder == 'desaturation'
            if stat.json_metadata.get('d1') == 3
            if stat.json_metadata.get('d2') == 15
            if stat.json_metadata.get('c') == 'end2end-inplace-distillation'
            if stat.json_metadata.get('p') == p
            if stat.json_metadata.get('r1') == 3
            if stat.json_metadata.get('r2') == 5
            if stat.json_metadata.get('noise') == 'uniform'
        ]
        if len(match_stats) == 1:
            gs = cultiv.stat_to_gap_stats(match_stats, rounding=1, func=lambda arg: sinter.AnonTaskStats(
                shots=arg.source.shots,
                discards=arg.at_least.discards + arg.less.shots,
                errors=arg.at_least.errors,
            ))
            c3n = gen.NoiseModel.uniform_depolarizing(p).noisy_circuit_skipping_mpp_boundaries(c3)
            for stat, cur_vol in select_rep_stats(gs, c3n):
                print(sinter.TaskStats(
                    strong_id=f'''ref{k}''',
                    json_metadata={**stat.json_metadata, 'c': f'''2024 This Work (d1=3)''', 'state': 'T', 'v': cur_vol},
                    decoder=stat.decoder,
                    shots=stat.shots,
                    errors=stat.errors * DOUBLING_S_GIVES_T_ASSUMPTION,
                    discards=stat.discards,
                ))
                k += 1
        else:
            print(f"WARNING: didn't find d1=3 d2=15 p={p} for historical data plot", file=sys.stderr)

    for p in [5e-4, 1e-3, 2e-3]:
        c5 = cultiv.make_end2end_cultivation_circuit(
            dcolor=5,
            dsurface=15,
            basis='Y',
            r_growing=5,
            r_end=1,
            inject_style='unitary',
        )
        match_stats = [
            stat
            for stat in new_stats
            if stat.decoder == 'desaturation'
            if stat.json_metadata.get('d1') == 5
            if stat.json_metadata.get('d2') == 15
            if stat.json_metadata.get('c') == 'end2end-inplace-distillation'
            if stat.json_metadata.get('p') == p
            if stat.json_metadata.get('r1') == 5
            if stat.json_metadata.get('r2') == 5
            if stat.json_metadata.get('noise') == 'uniform'
        ]
        if len(match_stats) == 1:
            gs = cultiv.stat_to_gap_stats(match_stats, rounding=1, func=lambda arg: sinter.AnonTaskStats(
                shots=arg.source.shots,
                discards=arg.at_least.discards + arg.less.shots,
                errors=arg.at_least.errors,
            ))
            c5n = gen.NoiseModel.uniform_depolarizing(p).noisy_circuit_skipping_mpp_boundaries(c5)
            for stat, cur_vol in select_rep_stats(gs, c5n):
                print(sinter.TaskStats(
                    strong_id=f'''ref{k}''',
                    json_metadata={**stat.json_metadata, 'c': f'''2024 This Work (d1=5)''', 'state': 'T', 'v': cur_vol},
                    decoder=stat.decoder,
                    shots=stat.shots,
                    errors=stat.errors * DOUBLING_S_GIVES_T_ASSUMPTION,
                    discards=stat.discards,
                ))
                k += 1
        else:
            print(f"WARNING: didn't find d1=5 d2=15 p={p} for historical data plot", file=sys.stderr)

    # CNOT reference (actual data).
    for p in [1e-3]:
        match_groups = sinter.group_by([
            stat
            for stat in new_stats
            if stat.decoder == 'sparse_blossom_correlated'
            if stat.json_metadata.get('c') == 'surface-code-cnot'
            if stat.json_metadata.get('p') == p
            if stat.json_metadata.get('noise') == 'uniform'
        ], key=lambda e: e.json_metadata['d2'])
        available_cnot = set()
        for _, (stat_x, stat_z) in match_groups.items():
            assert {stat_x.json_metadata['b'], stat_z.json_metadata['b']} == {'X', 'Z'}
            assert stat_x.discards == stat_z.discards == 0
            err_x = stat_x.errors / stat_x.shots
            err_z = stat_z.errors / stat_z.shots
            err_xz = 1 - (1 - err_x) * (1 - err_z)
            q = stat_x.json_metadata['q']
            d = stat_x.json_metadata['d2']
            r = stat_x.json_metadata['r']
            assert q == 2*d*d*3 + 4*d - 1  # sanity check size
            assert r == 2*d + 4
            if min(stat_x.errors, stat_z.errors) < 3:
                continue
            available_cnot.add(d)
            shots = min(stat_x.shots, stat_z.shots)
            print(sinter.TaskStats(
                strong_id=f'''ref{k}''',
                json_metadata={
                    **stat_x.json_metadata,
                    'c': f'''Lattice Surgery CNOT, for comparison''',
                    'state': 'CNOT',
                    'v': q * r,
                    'b': 'XZ',
                },
                decoder=stat_x.decoder,
                shots=shots,
                discards=0,
                errors=round(shots * err_xz)
            ))
            k += 1
    # CNOT reference (extrapolated).
    k += 1
    for d in range(3, 50, 2):
        if d in available_cnot:
            continue
        projected_error = 1.68 / 10 ** (d * 0.559)
        q = 2*d*d*3 + 4*d - 1
        r = 2*d + 4
        print(sinter.TaskStats(
            strong_id=f'''ref{k}''',
            decoder="projected",
            json_metadata={
                'q': q,
                'r': r,
                'p': 1e-3,
                'noise': 'uniform',
                'c': f'''Lattice Surgery CNOT, for comparison''',
                'state': 'CNOT',
                'v': q * r,
                'b': 'XZ',
                'projected': 1,
            },
            shots=round(100 / projected_error),
            errors=100,
            discards=0,
        ))
        k += 1


if __name__ == '__main__':
    main()