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Tests for dashboard_core.mitigation_runner.run_mitigation_sweep and
dashboard_core.mitigation_panel.build_panel_mitigation -- Phase 3 of the
dashboard refactor (ZNE + predictive healing as a real dashboard feature,
reusing dense_evolution.mitigation.zero_noise_extrapolation as-is).
"""
import matplotlib
matplotlib.use("Agg")
import numpy as np
import pytest
import dashboard_core as dc
BELL_CIRCUIT = "Bell |Φ+⟩"
def test_run_mitigation_sweep_ideal_raises():
with pytest.raises(ValueError, match="ideal"):
dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "ideal", 0.05, 256, 42,
)
def test_run_mitigation_sweep_returns_expected_keys():
res = dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
)
expected_keys = {
"noise_factors", "prob_per_scale", "fidelity_per_scale",
"prob_zne", "fidelity_zne", "prob_ideal", "healing_enabled",
"delta_preemp", "n_qubits",
}
assert expected_keys == set(res.keys())
assert res["noise_factors"] == (1.0, 2.0, 3.0)
assert len(res["prob_per_scale"]) == 3
assert len(res["fidelity_per_scale"]) == 3
assert res["healing_enabled"] is False
assert res["delta_preemp"] is None
def test_run_mitigation_sweep_prob_zne_normalized():
res = dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.08, 256, 7,
)
assert float(np.sum(res["prob_zne"])) == pytest.approx(1.0, abs=1e-6)
assert np.all(res["prob_zne"] >= 0.0)
def test_run_mitigation_sweep_plain_vs_healing_differ():
common = dict(source_mode="Libreria Built-in", circuit_name=BELL_CIRCUIT,
qasm_text="", noise_model="depolarizing", base_noise_p=0.08,
shots=256, seed=3)
plain = dc.run_mitigation_sweep(**common, healing_enabled=False)
healed = dc.run_mitigation_sweep(**common, healing_enabled=True)
assert healed["healing_enabled"] is True
assert healed["delta_preemp"] is not None
# not asserting a specific direction -- just that the healing branch is
# actually wired in and changes the result, not silently ignored
assert plain["fidelity_zne"] != pytest.approx(healed["fidelity_zne"], abs=1e-12)
def test_run_mitigation_sweep_healing_requires_three_factors():
with pytest.raises(ValueError, match="3"):
dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
noise_factors=(1.0, 2.0), healing_enabled=True,
)
def test_run_mitigation_sweep_fidelity_zne_directionally_sane():
# Error Mitigation (Real-Stress): a 15-qubit circuit with several
# noisy CX/RZ layers -- fidelity degrades noticeably as noise scales up,
# a case where ZNE has real signal to extrapolate against (unlike a
# single-CX Bell state, where fidelity barely moves across 1x-3x).
res = dc.run_mitigation_sweep(
"Libreria Built-in", "Error Mitigation (Real-Stress)", "",
"depolarizing", 0.06, 256, 11,
)
fid = res["fidelity_per_scale"]
# not asserting strict per-step monotonicity: NoiseModel.apply_to_sv
# draws its channel randomness from OS entropy for a JAX statevector
# (see registry.py's rng/jax_key docs), so a single-shot fidelity
# sample can plateau or tie between adjacent scales -- the precondition
# that actually matters is that noise measurably degraded fidelity
# overall (base vs. worst), not a smooth curve in between.
assert fid[0] > fid[-1], "expected fidelity to be lower at the highest noise scale than at the base scale in this fixture"
base_error = abs(1.0 - fid[0])
zne_error = abs(1.0 - res["fidelity_zne"])
assert zne_error < base_error, (
f"ZNE should move the estimate closer to ideal (F=1) than the raw "
f"base-noise sample: base_error={base_error:.4f}, zne_error={zne_error:.4f}"
)
def test_build_panel_mitigation():
mitigation_res = dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
)
base_res = dc.run_simulation(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
)
fig = dc.build_panel_mitigation(mitigation_res, base_res)
assert fig is not None
assert type(fig).__name__ == "Figure"
def test_build_panel_mitigation_with_healing():
mitigation_res = dc.run_mitigation_sweep(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
healing_enabled=True,
)
base_res = dc.run_simulation(
"Libreria Built-in", BELL_CIRCUIT, "", "depolarizing", 0.05, 256, 42,
)
fig = dc.build_panel_mitigation(mitigation_res, base_res)
assert fig is not None
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