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Unit tests for ui_pages/quantum_scars.py's numerical core (Hamiltonian
construction, exact-diagonalization propagation, and the two projection
strategies). Plain pytest against the module's private helpers directly --
no separate core module like dashboard_core.py exists for this page.
AppTest-level UI smoke testing was already done manually and is not
duplicated here.
"""
import matplotlib
matplotlib.use("Agg")
import numpy as np
import pytest
from ui_pages import quantum_scars as qs
N_QUBITS = 6 # dim=64, diagonalizes instantly
@pytest.fixture(scope="module")
def pxp():
return qs._build_pxp(N_QUBITS)
def _random_state(dim, seed):
rng = np.random.default_rng(seed)
sv = rng.normal(size=dim) + 1j * rng.normal(size=dim)
return sv / np.linalg.norm(sv)
# ── _is_valid_state ─────────────────────────────────────────────────────
def test_is_valid_state_rejects_adjacent_ones():
# 0b110000 has two adjacent 1-bits -> invalid
assert qs._is_valid_state(0b110000, N_QUBITS) is False
def test_is_valid_state_accepts_neel_pattern():
# 0b010101 alternates, never two adjacent 1-bits -> valid
assert qs._is_valid_state(0b010101, N_QUBITS) is True
def test_is_valid_state_all_zero_is_valid():
assert qs._is_valid_state(0, N_QUBITS) is True
def test_is_valid_state_matches_independent_bruteforce():
# Cross-check every state against an independent string-based
# implementation, unrelated to _build_pxp's own usage of the function.
for i in range(2 ** N_QUBITS):
expected = "11" not in format(i, f"0{N_QUBITS}b")
assert qs._is_valid_state(i, N_QUBITS) == expected
# ── _build_pxp ───────────────────────────────────────────────────────────
def test_build_pxp_hilbert_dimension(pxp):
assert pxp["dim"] == 2 ** N_QUBITS
assert pxp["eigenvalues"].shape == (pxp["dim"],)
assert pxp["eigenvectors"].shape == (pxp["dim"], pxp["dim"])
def test_build_pxp_hamiltonian_is_hermitian(pxp):
h = pxp["h_pxp"]
np.testing.assert_allclose(h, h.conj().T, atol=1e-10)
def test_build_pxp_valid_dim_bounded(pxp):
assert 0 < pxp["valid_dim"] <= pxp["dim"]
def test_build_pxp_tower_ceiling_bounded(pxp):
assert 0 < pxp["tower_ceiling"] <= 1 + 1e-9
# ── _propagate ───────────────────────────────────────────────────────────
def test_propagate_fidelity_one_at_t_zero(pxp):
neel = np.zeros(pxp["dim"], dtype=np.complex128)
neel[pxp["neel_idx"]] = 1.0
propagated = qs._propagate(pxp, neel, dt=0.0)
fidelity = np.abs(np.vdot(neel, propagated)) ** 2
assert fidelity == pytest.approx(1.0, abs=1e-9)
def test_propagate_preserves_norm(pxp):
neel = np.zeros(pxp["dim"], dtype=np.complex128)
neel[pxp["neel_idx"]] = 1.0
sv = neel.copy()
for _ in range(20):
sv = qs._propagate(pxp, sv, dt=0.2)
assert np.linalg.norm(sv) == pytest.approx(1.0, abs=1e-9)
# ── _project_tower ───────────────────────────────────────────────────────
def test_project_tower_stays_normalized(pxp):
sv = _random_state(pxp["dim"], seed=1)
projected = qs._project_tower(pxp, sv)
assert np.linalg.norm(projected) == pytest.approx(1.0, abs=1e-9)
def test_project_tower_zero_weight_outside_subspace(pxp):
sv = _random_state(pxp["dim"], seed=2)
projected = qs._project_tower(pxp, sv)
weight_in_tower = np.sum(np.abs(pxp["tower_vectors"].conj().T @ projected) ** 2)
assert weight_in_tower == pytest.approx(1.0, abs=1e-9)
def test_project_tower_orthogonal_state_returns_near_zero(pxp):
# Build a state supported only on eigenvectors NOT in the tower ->
# exercises the norm < 1e-12 safe-return branch without dividing by zero.
tower_set = set(np.argsort(
np.abs(pxp["eigenvectors"][pxp["neel_idx"], :]) ** 2
)[::-1][: N_QUBITS + 1].tolist())
non_tower_idx = next(i for i in range(pxp["dim"]) if i not in tower_set)
sv = pxp["eigenvectors"][:, non_tower_idx]
projected = qs._project_tower(pxp, sv)
assert np.linalg.norm(projected) < 1e-9
# ── _project_constraint ──────────────────────────────────────────────────
def test_project_constraint_stays_normalized(pxp):
sv = _random_state(pxp["dim"], seed=3)
projected = qs._project_constraint(pxp, sv)
assert np.linalg.norm(projected) == pytest.approx(1.0, abs=1e-9)
def test_project_constraint_zero_weight_on_invalid_states(pxp):
sv = _random_state(pxp["dim"], seed=4)
projected = qs._project_constraint(pxp, sv)
assert np.allclose(projected[~pxp["valid_mask"]], 0.0)
# ── _invalid_weight ───────────────────────────────────────────────────────
def test_invalid_weight_neel_state_is_zero(pxp):
neel = np.zeros(pxp["dim"], dtype=np.complex128)
neel[pxp["neel_idx"]] = 1.0
assert qs._invalid_weight(pxp, neel) == pytest.approx(0.0, abs=1e-12)
def test_invalid_weight_uniform_state_matches_complement_of_valid_dim(pxp):
uniform = np.ones(pxp["dim"], dtype=np.complex128) / np.sqrt(pxp["dim"])
expected = 1.0 - pxp["valid_dim"] / pxp["dim"]
assert qs._invalid_weight(pxp, uniform) == pytest.approx(expected, abs=1e-9)
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