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_generate_1q_state_tomography_experiment():
qubits = [0]
prog = Program(I(qubits[0]))
one_q_exp = generate_state_tomography_experiment(prog, qubits=qubits)
dimension = 2**len(qubits)
assert [one_q_exp[idx][0].out_operator[qubits[0]] for idx in range(0, dimension ** 2)] == \
['I', 'X', 'Y', 'Z']
def test_generate_2q_state_tomography_experiment():
p = Program()
p += H(0)
p += CZ(0, 1)
two_q_exp = generate_state_tomography_experiment(p, qubits=[0, 1])
dimension = 2**2
assert [str(two_q_exp[idx][0].out_operator) for idx in list(range(0, dimension ** 2))] == \
['(1+0j)*I', '(1+0j)*X1', '(1+0j)*Y1', '(1+0j)*Z1',
'(1+0j)*X0', '(1+0j)*X0*X1', '(1+0j)*X0*Y1', '(1+0j)*X0*Z1',
'(1+0j)*Y0', '(1+0j)*Y0*X1', '(1+0j)*Y0*Y1', '(1+0j)*Y0*Z1',
'(1+0j)*Z0', '(1+0j)*Z0*X1', '(1+0j)*Z0*Y1', '(1+0j)*Z0*Z1']
def run(qc: QuantumComputer, seq: List[Program], subgraph: List[List[int]], num_trials: int) -> np.ndarray:
prog = merge_programs(seq)
# TODO: parallelize
results = []
for qubits in subgraph:
state_prep = prog
tomo_exp = generate_state_tomography_experiment(state_prep, qubits=qubits)
_rs = list(measure_observables(qc, tomo_exp, num_trials))
# Inelegant shim from state tomo refactor. To clean up!
expectations=[r.expectation for r in _rs[1:]]
variances=[r.std_err ** 2 for r in _rs[1:]]
results.append((expectations, variances))
return results
def single_q_tomo_fixture():
qubits = [0]
qc = get_test_qc(n_qubits=len(qubits))
# Generate random unitary
u_rand = haar_rand_unitary(2 ** 1, rs=np.random.RandomState(52))
state_prep = Program().defgate("RandUnitary", u_rand)
state_prep.inst([("RandUnitary", qubits[0])])
# True state
wfn = NumpyWavefunctionSimulator(n_qubits=1)
psi = wfn.do_gate_matrix(u_rand, qubits=[0]).wf.reshape(-1)
rho_true = np.outer(psi, psi.T.conj())
# Get data from QVM
tomo_expt = generate_state_tomography_experiment(state_prep, qubits)
results = list(measure_observables(qc=qc, tomo_experiment=tomo_expt, n_shots=4000))
return results, rho_true
def two_q_tomo_fixture(test_qc):
qubits = [0, 1]
# Generate random unitary
u_rand1 = haar_rand_unitary(2 ** 1, rs=np.random.RandomState(52))
u_rand2 = haar_rand_unitary(2 ** 1, rs=np.random.RandomState(53))
state_prep = Program().defgate("RandUnitary1", u_rand1).defgate("RandUnitary2", u_rand2)
state_prep.inst(("RandUnitary1", qubits[0])).inst(("RandUnitary2", qubits[1]))
# True state
wfn = NumpyWavefunctionSimulator(n_qubits=2)
psi = wfn \
.do_gate_matrix(u_rand1, qubits=[0]) \
.do_gate_matrix(u_rand2, qubits=[1]) \
.wf.reshape(-1)
rho_true = np.outer(psi, psi.T.conj())
# Get data from QVM
tomo_expt = generate_state_tomography_experiment(state_prep, qubits)
results = list(estimate_observables(qc=test_qc, obs_expt=tomo_expt, num_shots=1000,
symm_type=-1))
results = list(calibrate_observable_estimates(test_qc, results))
return results, rho_true
