fix: suppress double measurement noise injection in MemoryCircuit

[huaweil-nv]

Summary

• Fix double measurement noise injection in MemoryCircuit when using the 25-parameter noise model
• _add_stabilizer_round(logical_measurement=True) correctly injects time-reversed measurement noise, then restores self.noise_model before returning. The subsequent add_measure() at line 1009 sees noise_model is not None and injects the same p_meas noise a second time, creating phantom error channels in the DEM
• Fix: temporarily clear self.noise_model around the final add_measure() call (3 lines added), matching the pattern already used inside _add_stabilizer_round itself

Impact

All inference users running with the default config_public.yaml 25-parameter noise model are affected. The bug is silent (no crash), but introduces phantom DEM error channels that do not correspond to any real physical noise process:

Distance	Phantom DEM Entries	DEM Total Prob Inflation
d=3	7 new	+4.12%
d=5	21 new	+2.13%
d=7	43 new	+1.45%

These phantom entries include spurious links to the logical observable (D20 L0, D21 L0), distorting PyMatching's matching graph weights and systematically biasing LER and threshold estimates.

Verification

Circuit-level (deterministic, no GPU needed):

PYTHONPATH=code python3 -c "
from qec.noise_model import NoiseModel
from qec.surface_code.memory_circuit import MemoryCircuit

nm = NoiseModel.from_config_dict({
    'p_prep_X': 0.002, 'p_prep_Z': 0.002, 'p_meas_X': 0.002, 'p_meas_Z': 0.002,
    'p_idle_cnot_X': 0.001, 'p_idle_cnot_Y': 0.001, 'p_idle_cnot_Z': 0.001,
    'p_idle_spam_X': 0.001998, 'p_idle_spam_Y': 0.001998, 'p_idle_spam_Z': 0.001998,
    'p_cnot_IX': 0.0002, 'p_cnot_IY': 0.0002, 'p_cnot_IZ': 0.0002,
    'p_cnot_XI': 0.0002, 'p_cnot_XX': 0.0002, 'p_cnot_XY': 0.0002,
    'p_cnot_XZ': 0.0002, 'p_cnot_YI': 0.0002, 'p_cnot_YX': 0.0002,
    'p_cnot_YY': 0.0002, 'p_cnot_YZ': 0.0002, 'p_cnot_ZI': 0.0002,
    'p_cnot_ZX': 0.0002, 'p_cnot_ZY': 0.0002, 'p_cnot_ZZ': 0.0002,
})
p = float(nm.get_max_probability())
for basis in ['X', 'Z']:
    circ = MemoryCircuit(distance=3, idle_error=p, sqgate_error=p, tqgate_error=p,
        spam_error=(2.0/3.0)*p, n_rounds=3, basis=basis, code_rotation='XV',
        noise_model=nm, add_boundary_detectors=True)
    lines = circ.circuit.strip().split('\n')
    mp = 'MZ' if basis == 'Z' else 'MX'
    idx = max(i for i,l in enumerate(lines) if l.strip().startswith(mp))
    err_type = 'X_ERROR' if basis == 'Z' else 'Z_ERROR'
    print(f'=== Basis {basis} ===')
    for l in lines[idx-5:idx]:
        print(l, '<<<< SPURIOUS' if err_type in l else '')
    print()
"

Before fix: X_ERROR(0.002) / Z_ERROR(0.002) present on all d² data qubits before final measurement.
After fix: No spurious noise lines before final measurement.

Test plan

• Circuit-level repro confirms spurious noise lines are removed
• DEM comparison confirms phantom error entries are eliminated
• WORKFLOW=inference bash code/scripts/local_run.sh runs successfully with fix applied

NVBug

https://nvbugspro.nvidia.com/bug/6059125

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[ivanbasov]

/ok to test fa7076d

[ivanbasov]

Two contributions on top of the fix:

---

1. Inline comment on the noise_model suppression (memory_circuit.py)

The three-line guard is non-obvious. Suggest adding a comment so future readers understand why it exists (see inline).

---

2. Regression test — test_noise_model.py

Add this method to TestNoiseModel. It parses the post-REPEAT circuit section and asserts exactly one measurement-error injection appears on data qubits — the legitimate fake-SPAM line — not two. Deterministic, CPU-only, would have caught this bug.

def test_no_double_measurement_noise_in_final_data_qubit_readout(self):
    """
    Regression test for double measurement-noise injection on data qubits at the end of
    MemoryCircuit.__init__ when using the 25-parameter NoiseModel.

    _add_stabilizer_round(logical_measurement=True) injects a single "fake SPAM" error on
    data qubits (time-reversed p_meas) and then restores self.noise_model before returning.
    Without the fix the subsequent add_measure(data_qubits) call at the __init__ call site
    would see a non-None noise_model and inject the same p_meas channel a *second* time,
    creating phantom DEM error entries that bias LER/threshold estimates.

    The fix suppresses noise_model around that add_measure call.  This test verifies that
    the post-REPEAT circuit section contains exactly ONE measurement-error injection on data
    qubits (the legitimate fake-SPAM line), not two.
    """
    D = 3
    T = 3  # n_rounds must be >= 3 for the circuit to use a REPEAT block
    nm = NoiseModel(
        p_prep_X=0.01,
        p_prep_Z=0.02,
        p_meas_X=0.03,  # non-zero: triggers double-injection if bug is present
        p_meas_Z=0.04,
        p_idle_cnot_X=0.002,
        p_idle_cnot_Y=0.001,
        p_idle_cnot_Z=0.003,
        p_idle_spam_X=0.002,
        p_idle_spam_Y=0.001,
        p_idle_spam_Z=0.003,
        **{f"p_cnot_{k}": 0.0005 for k in CNOT_ERROR_TYPES}
    )

    for basis in ("X", "Z"):
        circ = MemoryCircuit(
            distance=D,
            idle_error=nm.get_max_probability(),
            sqgate_error=nm.get_max_probability(),
            tqgate_error=nm.get_max_probability(),
            spam_error=nm.get_max_probability(),
            n_rounds=T,
            basis=basis,
            noise_model=nm,
            code_rotation="XV",
        )
        circ.set_error_rates()

        # Isolate the circuit section that appears after the REPEAT block.
        lines = circ.circuit.split("\n")
        in_repeat = False
        after_repeat = False
        post_repeat_lines = []
        for line in lines:
            stripped = line.strip()
            if stripped.startswith("REPEAT"):
                in_repeat = True
                continue
            if in_repeat and stripped == "}":
                in_repeat = False
                after_repeat = True
                continue
            if after_repeat:
                post_repeat_lines.append(stripped)

        # Basis-labelled semantics for data-qubit readout failure:
        #   X-basis measurement error -> Z_ERROR(p_meas_X)
        #   Z-basis measurement error -> X_ERROR(p_meas_Z)
        # The only legitimate occurrence in the post-REPEAT section is the single fake-SPAM
        # injection inside _add_stabilizer_round(logical_measurement=True).  A second line
        # with the same instruction is the regression.
        if basis == "X":
            error_instr = "Z_ERROR"
            p_meas = float(nm.p_meas_X)
        else:
            error_instr = "X_ERROR"
            p_meas = float(nm.p_meas_Z)

        meas_error_lines = [l for l in post_repeat_lines if l.startswith(error_instr)]
        self.assertEqual(
            len(meas_error_lines), 1,
            f"basis={basis}: expected exactly 1 {error_instr} line in post-REPEAT section "
            f"(fake-SPAM only), got {len(meas_error_lines)}. "
            f"Double injection would indicate the noise_model suppression fix is missing. "
            f"Lines: {meas_error_lines}"
        )
        # Confirm the single line carries the correct probability.
        expected_prefix = f"{error_instr}({p_meas:.10f})"
        self.assertTrue(
            meas_error_lines[0].startswith(expected_prefix),
            f"basis={basis}: expected {error_instr} with p={p_meas:.10f}, "
            f"got: {meas_error_lines[0]}"
        )

---

3. Fix two flaky CI failures — test_boundary_detectors.py

test_ler_improves_with_bd_noise_model and test_ler_improves_with_bd_all_orientations fail in CI on this branch. Root cause: both tests assert ler_with_bd < ler_no_bd (strict improvement). Those assertions were passing on main only because the phantom DEM entries from the bug artificially inflated no-BD LER. With the fix the phantom entries are gone; the true BD improvement is ~1–3%, well within the statistical noise of 10–20k independent samples (σ ≈ 1–3 errors). Verified empirically with 100k–200k samples: the difference is not reliably detectable at these parameters.

Fix: replace the strict assertLess / assertLessEqual with a 1.5× tolerance check. That bound is ~3σ detectable at these sample sizes and catches any genuine regression in BD logic.

test_ler_improves_with_bd_noise_model — replace the final assertion and update the docstring:

    def test_ler_improves_with_bd_noise_model(self):
        """Test that boundary detectors do not significantly degrade LER when using NoiseModel.

        NOTE on assertion strength: the LER improvement from boundary detectors is a marginal
        ~1-3% effect at these parameters.  Asserting strict improvement (ler_with_bd <
        ler_no_bd) is unreliable with sample sizes of 10k-50k because the two circuits are
        sampled independently and the difference is well within statistical noise.

        Before the double-measurement-noise fix the no-BD LER was *artificially* inflated by
        phantom DEM entries, which made the strict-less assertion pass coincidentally.  With the
        corrected DEM the true improvement is small and we instead verify the weaker property:
        boundary detectors must not increase LER by more than a factor of 1.5.
        """
        # ... (keep existing circuit setup unchanged) ...

        ratio = (ler_with_bd / ler_no_bd) if ler_no_bd > 0 else float("inf")
        print(f"  BD/no-BD ratio: {ratio:.2f}x")

        # Boundary detectors must not substantially degrade LER.
        self.assertLessEqual(
            ler_with_bd,
            ler_no_bd * 1.5,
            f"BD degraded LER by more than 1.5x: no_bd={ler_no_bd:.4e}, with_bd={ler_with_bd:.4e}"
        )

test_ler_improves_with_bd_all_orientations — same change to the inner assertion:

                self.assertLessEqual(
                    ler_with_bd,
                    ler_no_bd * 1.5,
                    f"rotation={rotation}: BD degraded LER by more than 1.5x: "
                    f"no_bd={ler_no_bd:.4e}, with_bd={ler_with_bd:.4e}"
                )

[ivanbasov]

/ok to test 28ad1db

[ivanbasov]

/ok to test 016ed5e