def test_group_experiments():
expts = [ # cf above, I removed the inner nesting. Still grouped visually
ExperimentSetting(sI(),
sX(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sX(1)),
ExperimentSetting(sI(),
sZ(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sZ(1)),
]
suite = TomographyExperiment(expts, Program(), qubits=[0, 1])
grouped_suite = group_experiments(suite)
assert len(suite) == 4
assert len(grouped_suite) == 2
def test_measure_observables(forest):
expts = [
ExperimentSetting(sI(), o1 * o2)
for o1, o2 in itertools.product([sI(0), sX(0), sY(0), sZ(0)], [sI(1), sX(1), sY(1), sZ(1)])
]
suite = TomographyExperiment(expts, program=Program(X(0), CNOT(0, 1)), qubits=[0, 1])
assert len(suite) == 4 * 4
gsuite = group_experiments(suite)
assert len(gsuite) == 3 * 3 # can get all the terms with I for free in this case
qc = get_qc('2q-qvm')
for res in measure_observables(qc, gsuite, n_shots=10_000):
if res.setting.out_operator in [sI(), sZ(0), sZ(1), sZ(0) * sZ(1)]:
assert np.abs(res.expectation) > 0.9
else:
assert np.abs(res.expectation) < 0.1
def test_qc_expectation_on_qvm(client_configuration: QCSClientConfiguration, dummy_compiler: DummyCompiler):
# regression test for https://github.com/rigetti/forest-tutorials/issues/2
qc = QuantumComputer(name="testy!", qam=QVM(client_configuration=client_configuration), compiler=dummy_compiler)
p = Program()
theta = p.declare("theta", "REAL")
p += RESET()
p += RY(theta, 0)
p.wrap_in_numshots_loop(10000)
sx = ExperimentSetting(in_state=_pauli_to_product_state(sZ(0)), out_operator=sX(0))
e = Experiment(settings=[sx], program=p)
thetas = [-np.pi / 2, 0.0, np.pi / 2]
results = []
# Verify that multiple calls to qc.experiment with the same experiment backed by a QVM that
# requires_exectutable does not raise an exception.
for theta in thetas:
results.append(qc.experiment(e, memory_map={"theta": [theta]}))
assert np.isclose(results[0][0].expectation, -1.0, atol=0.01)
assert np.isclose(results[0][0].std_err, 0)
assert results[0][0].total_counts == 20000
# bounds on atol and std_err here are a little loose to try and avoid test flakiness.
assert np.isclose(results[1][0].expectation, 0.0, atol=0.1)
assert results[1][0].std_err < 0.01
assert results[1][0].total_counts == 20000
assert np.isclose(results[2][0].expectation, 1.0, atol=0.01)
assert np.isclose(results[2][0].std_err, 0)
assert results[2][0].total_counts == 20000
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_deser(tmpdir):
expts = [
[
ExperimentSetting(TensorProductState(), sX(0) * sI(1)),
ExperimentSetting(TensorProductState(), sI(0) * sX(1)),
],
[
ExperimentSetting(TensorProductState(), sZ(0) * sI(1)),
ExperimentSetting(TensorProductState(), sI(0) * sZ(1)),
],
]
suite = Experiment(settings=expts, program=Program(X(0), Y(1)))
to_json(f"{tmpdir}/suite.json", suite)
suite2 = read_json(f"{tmpdir}/suite.json")
assert suite == suite2
def test_stats_from_measurements():
d_results = {0: np.array([0] * 10), 1: np.array([1] * 10)}
setting = ExperimentSetting(TensorProductState(), sZ(0) * sX(1))
n_shots = 1000
obs_mean, obs_var = _stats_from_measurements(d_results, setting, n_shots)
assert obs_mean == -1.0
assert obs_var == 0.0
def test_is_identity():
pt1 = -1.5j * sI(2)
pt2 = 1.5 * sX(1) * sZ(2)
assert is_identity(pt1)
assert is_identity(pt2 + (-1 * pt2) + sI(0))
assert not is_identity(0 * pt1)
assert not is_identity(pt2 + (-1 * pt2))
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 train_xor(samples):
param = [np.pi,0,0,np.pi,np.pi,np.pi,0,0,np.pi]
s = samples[2]
sec_reg = QubitPlaceholder()
val = sim.expectation(address_qubits(fun_xor(s[1], param, s[0])), [sZ(7)]).real[0]
print(address_qubits(fun_xor(s[1], param, s[0])))
print(val)
print(s[2])
print(abs(val-s[2]))
# x = sim.expectation(address_qubits(p), [sZ(3)])
# print(address_qubits(fun_xor(s[1], sec_reg, s[2], param, s[0])))
# x = sim.expectation(address_qubits(fun_xor(s[1], s[3], x, s[0])), )
# for s in samples:
fun = lambda x: abs(sim.expectation(address_qubits(fun_xor(s[1], param, s[0])), [sZ(7)]).real[0] - s[2])
# fun = lambda x: sim.expectation(address_qubits(fun_xor(s[1], sec_reg, s[2], x, s[0]), [sZ(8)*sZ(0)]))
res = minimize(fun, np.array(param), method="Nelder-Mead", tol=10**-6)
return res.x, res.fun
def test_setting_no_in():
out_ops = _generate_random_paulis(n_qubits=4, n_terms=7)
for oop in out_ops:
expt = ExperimentSetting(zeros_state(oop.get_qubits()), oop)
expt2 = ExperimentSetting.from_str(str(expt))
assert expt == expt2
assert expt2.in_operator == functools.reduce(mul, [sZ(q) for q in oop.get_qubits()], sI())
assert expt2.out_operator == oop
def test_stabilizer_projection_Z():
"""
test if we project out the correct state
"""
stabilizer_state = project_stabilized_state([sZ(0)])
true_state = np.zeros((2, 1))
true_state[0, 0] = 1
assert np.allclose(true_state, stabilizer_state.todense())
def test_tomo_experiment_pre_grouped():
expts = [
[ExperimentSetting(sI(), sX(0) * sI(1)), ExperimentSetting(sI(), sI(0) * sX(1))],
[ExperimentSetting(sI(), sZ(0) * sI(1)), ExperimentSetting(sI(), sI(0) * sZ(1))],
]
suite = TomographyExperiment(
settings=expts,
program=Program(X(0), Y(1)),
qubits=[0, 1]
)
assert len(suite) == 2 # number of groups
for es1, es2 in zip(expts, suite):
for e1, e2 in zip(es1, es2):
assert e1 == e2
prog_str = str(suite).splitlines()[0]
assert prog_str == 'X 0; Y 1'
def test_expectation(forest: ForestConnection):
# The forest fixture (argument) to this test is to ensure this is
# skipped when a forest web api key is unavailable. You could also
# pass it to the constructor of WavefunctionSimulator() but it is not
# necessary.
wfnsim = WavefunctionSimulator()
bell = Program(
H(0),
CNOT(0, 1),
)
expects = wfnsim.expectation(bell, [
sZ(0) * sZ(1),
sZ(0),
sZ(1),
sX(0) * sX(1),
])
assert expects.size == 4
np.testing.assert_allclose(expects, [1, 0, 0, 1])
def test_multiaddress():
p = Program()
q0, q1 = [QubitPlaceholder() for _ in range(2)]
p += exponential_map(sZ(q0) * sZ(q1))(0.5)
map1 = {q0: 0, q1: 1}
map2 = {q0: 9, q1: 10}
p1 = address_qubits(p, map1)
with pytest.raises(RuntimeError):
_ = p.out() # make sure the original isn't affected
assert p1.out() == "CNOT 0 1\nRZ(1.0) 1\nCNOT 0 1\n"
p2 = address_qubits(p, map2)
assert p1.out() == "CNOT 0 1\nRZ(1.0) 1\nCNOT 0 1\n"
assert p2.out() == "CNOT 9 10\nRZ(1.0) 10\nCNOT 9 10\n"
def test_expt_settings_diagonal_in_tpb():
def _expt_settings_diagonal_in_tpb(es1: ExperimentSetting, es2: ExperimentSetting):
"""
Extends the concept of being diagonal in the same tpb to ExperimentSettings, by
determining if the pairs of in_states and out_operators are separately diagonal in the same
tpb
"""
max_weight_in = _max_weight_state([es1.in_state, es2.in_state])
max_weight_out = _max_weight_operator([es1.out_operator, es2.out_operator])
return max_weight_in is not None and max_weight_out is not None
expt_setting1 = ExperimentSetting(plusZ(1) * plusX(0), sY(1) * sZ(0))
expt_setting2 = ExperimentSetting(plusY(2) * plusZ(1), sZ(2) * sY(1))
assert _expt_settings_diagonal_in_tpb(expt_setting1, expt_setting2)
expt_setting3 = ExperimentSetting(plusX(2) * plusZ(1), sZ(2) * sY(1))
expt_setting4 = ExperimentSetting(plusY(2) * plusZ(1), sX(2) * sY(1))
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting3)
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting4)
def test_experiment_suite():
expts = [
ExperimentSetting(sI(),
sX(0) * sY(1)),
ExperimentSetting(sZ(0), sZ(0)),
]
suite = TomographyExperiment(settings=expts,
program=Program(X(0), Y(1)),
qubits=[0, 1])
assert len(suite) == 2
for e1, e2 in zip(expts, suite):
# experiment suite puts in groups of length 1
assert len(e2) == 1
e2 = e2[0]
assert e1 == e2
prog_str = str(suite).splitlines()[0]
assert prog_str == 'X 0; Y 1'
def test_expectation_helper():
n_qubits = 3
wf = np.zeros(shape=((2, ) * n_qubits), dtype=np.complex)
wf[0, 0, 0] = 1
z0 = _term_expectation(wf, 0.4 * sZ(0))
assert z0 == 0.4
x0 = _term_expectation(wf, sX(2))
assert x0 == 0
def test_expt_settings_share_ntpb():
expts = [[
ExperimentSetting(zeros_state([0, 1]),
sX(0) * sI(1)),
ExperimentSetting(zeros_state([0, 1]),
sI(0) * sX(1))
],
[
ExperimentSetting(zeros_state([0, 1]),
sZ(0) * sI(1)),
ExperimentSetting(zeros_state([0, 1]),
sI(0) * sZ(1))
]]
for group in expts:
for e1, e2 in itertools.combinations(group, 2):
assert _max_weight_state([e1.in_state, e2.in_state]) is not None
assert _max_weight_operator([e1.out_operator,
e2.out_operator]) is not None
def test_expt_settings_diagonal_in_tpb():
expt_setting1 = ExperimentSetting(sZ(1) * sX(0), sY(1) * sZ(0))
expt_setting2 = ExperimentSetting(sY(2) * sZ(1), sZ(2) * sY(1))
assert _expt_settings_diagonal_in_tpb(expt_setting1, expt_setting2)
expt_setting3 = ExperimentSetting(sX(2) * sZ(1), sZ(2) * sY(1))
expt_setting4 = ExperimentSetting(sY(2) * sZ(1), sX(2) * sY(1))
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting3)
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting4)
def test_rotation_programs():
"""
Testing the generation of post rotations
"""
test_term = sZ(0) * sX(20) * sI(100) * sY(5)
# note: string comparison of programs requires gates to be in the same order
true_rotation_program = Program().inst(
[RX(np.pi / 2)(5), RY(-np.pi / 2)(20)])
test_rotation_program = get_rotation_program(test_term)
assert true_rotation_program.out() == test_rotation_program.out()
def test_projector_generator():
"""
Test if we are getting accurate projectors--multiqubit case
"""
true_zero_projector = 0.5 * (sZ(0) + sI(0))
zero_projector = projector_generator([0], [0])
assert true_zero_projector == zero_projector
one_projector = projector_generator([1], [1])
true_one_projector = 0.5 * (sI(0) - sZ(0))
assert true_one_projector == one_projector
lowering_projector = projector_generator([0], [1])
true_lowering_projector = 0.5 * (sX(0) + 1j * sY(0))
assert true_lowering_projector == lowering_projector
raising_projector = projector_generator([1], [0])
true_raising_projector = 0.5 * (sX(0) - 1j * sY(0))
assert true_raising_projector == raising_projector
def test_build_symmetrization_memory_maps():
p = Program()
s = ExperimentSetting(in_state=sZ(0) * sZ(1), out_operator=sZ(0) * sZ(1))
e = TomographyExperiment(settings=[s], program=p)
memory_maps = [
{
"symmetrization": [0.0, 0.0]
},
{
"symmetrization": [0.0, np.pi]
},
{
"symmetrization": [np.pi, 0.0]
},
{
"symmetrization": [np.pi, np.pi]
},
]
assert e.build_symmetrization_memory_maps([0, 1]) == memory_maps
def test_term_powers():
for qubit in QubitPlaceholder.register(2):
pauli_terms = [sI(qubit), sX(qubit), sY(qubit), sZ(qubit)]
for pauli_term in pauli_terms:
assert pauli_term**0 == sI(qubit)
assert pauli_term**1 == pauli_term
assert pauli_term**2 == sI(qubit)
assert pauli_term**3 == pauli_term
with pytest.raises(ValueError):
pauli_terms[0]**-1
def error(order, time_step_length):
a_pauli = time_step_length * sZ(0) * sY(1) * sX(2)
a_program = a_pauli.program
b_pauli = time_step_length * sX(0) * sZ(1) * sY(2)
b_program = b_pauli.program
num_qubits = len(a_program.get_qubits())
assert num_qubits == len(b_program.get_qubits())
a = program_unitary(a_program, num_qubits)
b = program_unitary(b_program, num_qubits)
a_plus_b = a + b
exp_a_plus_b = expmi(time_step_length * a_plus_b)
trotter_program = trotterize(a_pauli, b_pauli, trotter_order=order)
trotter = program_unitary(trotter_program, num_qubits)
return np.linalg.norm(exp_a_plus_b - trotter, np.inf)
def init_ham(N_, D_):
global N
global D
global n
global q
N = N_
D = D_
if D_ == 2:
n = 4 * N_ - 10
q = [sZ(x) for x in range(n)]
def test_max_tpb_overlap_3():
# add another ExperimentSetting to the above
expt_setting = ExperimentSetting(PauliTerm.from_compact_str('(1+0j)*Z7Y8Z1Y4Z2Y5Y0X6'),
PauliTerm.from_compact_str('(1+0j)*Z4X8Y5X3Y7Y1'))
expt_setting2 = ExperimentSetting(sZ(7), sY(1))
p = Program(H(0), H(1), H(2))
qubits = [0, 1, 2]
tomo_expt2 = TomographyExperiment([expt_setting, expt_setting2], p, qubits)
expected_dict2 = {expt_setting: [expt_setting, expt_setting2]}
assert expected_dict2 == _max_tpb_overlap(tomo_expt2)
def test_tomo_experiment_pre_grouped():
expts = [
[
ExperimentSetting(TensorProductState(), sX(0) * sI(1)),
ExperimentSetting(TensorProductState(), sI(0) * sX(1)),
],
[
ExperimentSetting(TensorProductState(), sZ(0) * sI(1)),
ExperimentSetting(TensorProductState(), sI(0) * sZ(1)),
],
]
suite = Experiment(settings=expts, program=Program(X(0), Y(1)))
assert len(suite) == 2 # number of groups
for es1, es2 in zip(expts, suite):
for e1, e2 in zip(es1, es2):
assert e1 == e2
prog_str = str(suite).splitlines()[3:5]
assert prog_str == EXPERIMENT_REPR.splitlines()[4:6]
def test_all_ops_belong_to_tpb():
expts = [
[
ExperimentSetting(sI(),
sX(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sX(1))
],
[
ExperimentSetting(sI(),
sZ(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sZ(1))
],
]
for group in expts:
for e1, e2 in itertools.combinations(group, 2):
assert _all_qubits_diagonal_in_tpb(e1.in_operator, e2.in_operator)
assert _all_qubits_diagonal_in_tpb(e1.out_operator,
e2.out_operator)
def test_group_experiments_greedy():
ungrouped_tomo_expt = TomographyExperiment([[
ExperimentSetting(
PauliTerm.from_compact_str('(1+0j)*Z7Y8Z1Y4Z2Y5Y0X6'),
PauliTerm.from_compact_str('(1+0j)*Z4X8Y5X3Y7Y1'))
], [ExperimentSetting(sZ(7), sY(1))]],
program=Program(
H(0), H(1), H(2)),
qubits=[0, 1, 2])
grouped_tomo_expt = group_experiments_greedy(ungrouped_tomo_expt)
expected_grouped_tomo_expt = TomographyExperiment([[
ExperimentSetting(
PauliTerm.from_compact_str('(1+0j)*Z7Y8Z1Y4Z2Y5Y0X6'),
PauliTerm.from_compact_str('(1+0j)*Z4X8Y5X3Y7Y1')),
ExperimentSetting(sZ(7), sY(1))
]],
program=Program(
H(0), H(1), H(2)),
qubits=[0, 1, 2])
assert grouped_tomo_expt == expected_grouped_tomo_expt
def test_append():
expts = [
[
ExperimentSetting(sI(),
sX(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sX(1))
],
[
ExperimentSetting(sI(),
sZ(0) * sI(1)),
ExperimentSetting(sI(),
sI(0) * sZ(1))
],
]
suite = TomographyExperiment(settings=expts,
program=Program(X(0), Y(1)),
qubits=[0, 1])
suite.append(ExperimentSetting(sI(), sY(0) * sX(1)))
assert (len(str(suite))) > 0
from pyquil.paulis import ID, sX, sY, sZ
from pyquil import *
from pyquil.gates import H
import pyquil.paulis as pl
# Pauli term takes an operator "X", "Y", "Z", or "I"; a qubit to act on, and
# an optional coefficient.
a = 1 * ID()
b = -0.75 * sX(0) * sY(1) * sZ(3)
c = (5-2j) * sZ(1) * sX(2)
# Construct a sum of Pauli terms.
sigma = a + b + c
print("sigma = {}".format(sigma))
p = Program()
p.inst(pl.exponentiate_commuting_pauli_sum(sigma))
print(p)