def test_R_operator_with_hand_calc_example_1_qubit():
# This example was worked out by hand
rho = ID / 2
obs_freqs = [3, 7]
my_by_hand_calc_ans_Z = ((3 / 0.5) * PROJ_ZERO +
(7 / 0.5) * PROJ_ONE) / sum(obs_freqs)
my_by_hand_calc_ans_X = ((3 / 0.5) * PROJ_PLUS +
(7 / 0.5) * PROJ_MINUS) / sum(obs_freqs)
qubits = [Q0]
exp = (obs_freqs[0] - obs_freqs[1]) / sum(obs_freqs)
zplus_result = ExperimentResult(setting=Z_SETTING,
expectation=exp,
total_counts=sum(obs_freqs))
xplus_result = ExperimentResult(setting=X_SETTING,
expectation=exp,
total_counts=sum(obs_freqs))
z_results = [zplus_result]
x_results = [xplus_result]
# Z basis test
np.testing.assert_allclose(_R(rho, z_results, qubits),
my_by_hand_calc_ans_Z,
atol=1e-12)
# X basis test
np.testing.assert_allclose(_R(rho, x_results, qubits),
my_by_hand_calc_ans_X,
atol=1e-12)
def test_R_operator_fixed_point_1_qubit():
# Check fixed point of operator. See Eq. 5 in Řeháček et al., PRA 75, 042108 (2007).
qubits = [Q0]
id_result = ExperimentResult(setting=ID_SETTING,
expectation=1,
total_counts=1)
zplus_result = ExperimentResult(setting=Z_SETTING,
expectation=1,
total_counts=1)
xplus_result = ExperimentResult(setting=X_SETTING,
expectation=1,
total_counts=1)
z_results = [id_result, zplus_result]
x_results = [id_result, xplus_result]
def test_trace(rho, results):
return _R(rho, results, qubits) @ rho @ _R(rho, results, qubits)
np.testing.assert_allclose(test_trace(PROJ_ZERO, z_results),
PROJ_ZERO,
atol=1e-12)
np.testing.assert_allclose(test_trace(PROJ_PLUS, x_results),
PROJ_PLUS,
atol=1e-12)
def _resample_expectations_with_beta(results, prior_counts=1):
"""Resample expectation values by constructing a beta distribution and sampling from it.
Used by :py:func:`estimate_variance`.
:param results: A list of ExperimentResults
:param prior_counts: Number of "counts" to add to alpha and beta for the beta distribution
from which we sample.
:return: A new list of ``results`` where each ExperimentResult's ``expectation`` field
contained a resampled expectation value
"""
resampled_results = []
for result in results:
# reconstruct the raw counts of observations from the pauli observable mean
num_plus = ((result.expectation + 1) / 2) * result.total_counts
num_minus = result.total_counts - num_plus
# We resample this data assuming it was from a beta distribution,
# with additive smoothing
alpha = num_plus + prior_counts
beta = num_minus + prior_counts
# transform bit bias back to pauli expectation value
resampled_expect = 2 * np.random.beta(alpha, beta) - 1
resampled_results += [ExperimentResult(
setting=result.setting,
expectation=resampled_expect,
std_err=result.std_err,
total_counts=result.total_counts,
)]
return resampled_results
def test_experiment_result():
er = ExperimentResult(
setting=ExperimentSetting(sX(0), sZ(0)),
expectation=0.9,
stddev=0.05,
)
assert str(er) == '(1+0j)*X0→(1+0j)*Z0: 0.9 +- 0.05'
def test_experiment_result():
er = ExperimentResult(
setting=ExperimentSetting(plusX(0), sZ(0)),
expectation=0.9,
stddev=0.05,
total_counts=100,
)
assert str(er) == 'X0_0→(1+0j)*Z0: 0.9 +- 0.05'
def test_R_operator_fixed_point_2_qubit():
# Check fixed point of operator. See Eq. 5 in Řeháček et al., PRA 75, 042108 (2007).
qubits = [0, 1]
id_setting = ExperimentSetting(in_state=zeros_state(qubits),
out_operator=sI(qubits[0]) * sI(qubits[1]))
zz_setting = ExperimentSetting(in_state=zeros_state(qubits),
out_operator=sZ(qubits[0]) * sI(qubits[1]))
id_result = ExperimentResult(setting=id_setting,
expectation=1,
total_counts=1)
zzplus_result = ExperimentResult(setting=zz_setting,
expectation=1,
total_counts=1)
zz_results = [id_result, zzplus_result]
# Z basis test
r = _R(P00, zz_results, qubits)
actual = r @ P00 @ r
np.testing.assert_allclose(actual, P00, atol=1e-12)
def wfn_measure_observables(n_qubits, tomo_expt: TomographyExperiment):
if len(tomo_expt.program.defined_gates) > 0:
raise pytest.skip("Can't do wfn on defined gates yet")
wfn = NumpyWavefunctionSimulator(n_qubits)
for settings in tomo_expt:
for setting in settings:
prog = Program()
for oneq_state in setting.in_state.states:
prog += _one_q_state_prep(oneq_state)
prog += tomo_expt.program
yield ExperimentResult(
setting=setting,
expectation=wfn.reset().do_program(prog).expectation(setting.out_operator),
stddev=0.,
total_counts=1, # don't set to zero unless you want nans
)