def test_exhaustive_state_dfe_run(benchmarker: BenchmarkConnection):
wfnsim = NumpyWavefunctionSimulator(n_qubits=1)
process = Program(X(0))
texpt = generate_exhaustive_state_dfe_experiment(program=process,
qubits=[0],
benchmarker=benchmarker)
for setting in texpt:
setting = setting[0]
prog = Program()
for oneq_state in setting.in_state.states:
prog += _one_q_state_prep(oneq_state)
prog += process
expectation = wfnsim.reset().do_program(prog).expectation(
setting.out_operator)
assert expectation == 1.
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
)
def test_monte_carlo_process_dfe(benchmarker: BenchmarkConnection):
process = Program(CNOT(0, 1))
texpt = generate_monte_carlo_process_dfe_experiment(
program=process, qubits=[0, 1], n_terms=10, benchmarker=benchmarker)
assert len(texpt) == 10
wfnsim = NumpyWavefunctionSimulator(n_qubits=2)
for setting in texpt:
setting = setting[0]
prog = Program()
for oneq_state in setting.in_state.states:
prog += _one_q_state_prep(oneq_state)
prog += process
expectation = wfnsim.reset().do_program(prog).expectation(
setting.out_operator)
assert_almost_equal(expectation, 1., decimal=7)
