def test_variance_bootstrap():
qubits = [0, 1]
qc = get_test_qc(n_qubits=len(qubits))
state_prep = Program([H(q) for q in qubits])
state_prep.inst(CZ(qubits[0], qubits[1]))
tomo_expt = generate_state_tomography_experiment(state_prep, qubits)
results = list(
measure_observables(qc=qc, tomo_experiment=tomo_expt, n_shots=4000))
estimate, status = iterative_mle_state_estimate(results=results,
qubits=qubits,
dilution=0.5)
rho_est = estimate.estimate.state_point_est
purity = np.trace(rho_est @ rho_est)
purity = np.real_if_close(purity)
assert purity.imag == 0.0
def my_mle_estimator(_r, _q):
return iterative_mle_state_estimate(results=_r,
qubits=_q,
dilution=0.5,
entropy_penalty=0.0,
beta=0.0)[0]
boot_purity, boot_var = estimate_variance(results=results,
qubits=qubits,
tomo_estimator=my_mle_estimator,
functional=dm.purity,
n_resamples=5,
project_to_physical=False)
np.testing.assert_allclose(purity,
boot_purity,
atol=2 * np.sqrt(boot_var),
rtol=0.01)
def test_maxent_two_qubit(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
rho_est = iterative_mle_state_estimate(results=results, qubits=qubits, entropy_penalty=.001,
tol=1e-5)
np.testing.assert_allclose(rho_true, rho_est, atol=0.02)
def test_hedged_two_qubit(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
estimate, status = iterative_mle_state_estimate(results=results, qubits=qubits,
dilution=0.5, beta=0.5)
rho_est = estimate.estimate.state_point_est
np.testing.assert_allclose(rho_true, rho_est, atol=0.01)
def test_maxent_two_qubit(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
estimate, status = iterative_mle_state_estimate(results=results, qubits=qubits,
dilution=0.5, entropy_penalty=1.0, tol=1e-5)
rho_est = estimate.estimate.state_point_est
np.testing.assert_allclose(rho_true, rho_est, atol=0.01)
def test_variance_bootstrap(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
rho_est = iterative_mle_state_estimate(results=results,
qubits=qubits,
tol=1e-4)
purity = np.trace(rho_est @ rho_est)
purity = np.real_if_close(purity)
assert purity.imag == 0.0
faster_tomo_est = partial(iterative_mle_state_estimate,
epsilon=.0001,
beta=.5,
tol=1e-3)
boot_purity, boot_var = estimate_variance(results=results,
qubits=qubits,
tomo_estimator=faster_tomo_est,
functional=dm.purity,
n_resamples=50,
project_to_physical=False)
np.testing.assert_allclose(purity,
boot_purity,
atol=2 * np.sqrt(boot_var),
rtol=0.01)
def test_single_qubit_mle(single_q_tomo_fixture):
qubits = [0]
results, rho_true = single_q_tomo_fixture
estimate, status = iterative_mle_state_estimate(results=results,
qubits=qubits,
dilution=0.5)
rho_est = estimate.estimate.state_point_est
np.testing.assert_allclose(rho_true, rho_est, atol=0.01)
def test_hedged_two_qubit(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
rho_est = iterative_mle_state_estimate(results=results,
qubits=qubits,
epsilon=.0001,
beta=0.5,
tol=1e-3)
np.testing.assert_allclose(rho_true, rho_est, atol=0.02)
def my_mle_estimator(_r, _q):
return iterative_mle_state_estimate(results=_r,
qubits=_q,
dilution=0.5,
entropy_penalty=0.0,
beta=0.0)[0]
def test_two_qubit_mle(two_q_tomo_fixture):
qubits = [0, 1]
results, rho_true = two_q_tomo_fixture
rho_est = iterative_mle_state_estimate(results=results, qubits=qubits)
np.testing.assert_allclose(rho_true, rho_est, atol=0.01)
