def test_qobj_save_load(self):
filename = self._get_resource_path('qobj/cpp_save_load.json')
with open(filename, 'r') as file:
qobj = Qobj.from_dict(json.load(file))
result = self.backend.run(qobj).result()
snapshots = result.data('save_command')['snapshots']['statevector']
self.assertEqual(set(snapshots), {'0', '1', '10', '11'},
msg='snapshot keys')
state0 = format_statevector(snapshots['0'][0])
state10 = format_statevector(snapshots['10'][0])
state1 = format_statevector(snapshots['1'][0])
state11 = format_statevector(snapshots['11'][0])
expected_state0 = np.array([1, 0])
expected_state10 = np.array([1 / np.sqrt(2), 1 / np.sqrt(2)])
fidelity0 = np.abs(expected_state0.dot(state0.conj())) ** 2
fidelity1 = np.abs(expected_state0.dot(state1.conj())) ** 2
fidelity10 = np.abs(expected_state10.dot(state10.conj())) ** 2
fidelity11 = np.abs(expected_state10.dot(state11.conj())) ** 2
self.assertAlmostEqual(fidelity0, 1.0, places=10, msg='snapshot 0')
self.assertAlmostEqual(fidelity10, 1.0, places=10, msg='snapshot 0')
self.assertAlmostEqual(fidelity1, 1.0, places=10, msg='snapshot 0')
self.assertAlmostEqual(fidelity11, 1.0, places=10, msg='snapshot 0')
def test_combine_circuit_extension_instructions(self):
"""Test combining circuits containing barrier, initializer, snapshot
"""
qr = QuantumRegister(2)
cr = ClassicalRegister(2)
qc1 = QuantumCircuit(qr)
desired_vector = [0.5 + 0.j, 0.5 + 0.j, 0.5 + 0.j, 0.5 + 0.j]
qc1.initialize(desired_vector, qr)
qc1.barrier()
qc2 = QuantumCircuit(qr, cr)
qc2.snapshot(slot='1')
qc2.measure(qr, cr)
new_circuit = qc1 + qc2
backend = Aer.get_backend('qasm_simulator')
shots = 1024
result = execute(new_circuit, backend=backend, shots=shots,
seed=78).result()
snapshot_vectors = result.data(0)['snapshots']['statevector']['1']
snapshot = format_statevector(snapshot_vectors[0])
fidelity = state_fidelity(snapshot, desired_vector)
self.assertGreater(fidelity, 0.99)
counts = result.get_counts()
target = {
'00': shots / 4,
'01': shots / 4,
'10': shots / 4,
'11': shots / 4
}
threshold = 0.04 * shots
self.assertDictAlmostEqual(counts, target, threshold)
def get_counts(self, experiment=None):
"""Get the histogram data of an experiment.
Args:
experiment (str or QuantumCircuit or Schedule or int or None): the index of the
experiment, as specified by ``get_data()``.
Returns:
dict[str:int]: a dictionary with the counts for each qubit, with
the keys containing a string in binary format and separated
according to the registers in circuit (e.g. ``0100 1110``).
The string is little-endian (cr[0] on the right hand side).
Raises:
QiskitError: if there are no counts for the experiment.
"""
exp = self._get_experiment(experiment)
try:
header = exp.header.to_dict()
except (AttributeError, QiskitError): # header is not available
header = None
if 'counts' in self.data(experiment).keys():
return postprocess.format_counts(
self.data(experiment)['counts'], header)
elif 'statevector' in self.data(experiment).keys():
vec = postprocess.format_statevector(
self.data(experiment)['statevector'])
return state_to_counts(vec)
else:
raise QiskitError(
'No counts for experiment "{0}"'.format(experiment))
def test_conditionals(self):
filename = self._get_resource_path('qobj/cpp_conditionals.json')
with open(filename, 'r') as file:
qobj = Qobj.from_dict(json.load(file))
result = self.backend.run(qobj).result()
expected_data = {
'single creg (c0=0)': {
'statevector': np.array([1, 0, 0, 0])},
'single creg (c0=1)': {
'statevector': np.array([0, 0, 0, 1])},
'two creg (c1=0)': {
'statevector': np.array([1, 0, 0, 0])},
'two creg (c1=1)': {
'statevector': np.array([0, 0, 0, 1])}
}
for name in expected_data:
# Check snapshot
snapshots = result.data(name)['snapshots']['statevector']
self.assertEqual(set(snapshots), {'0'},
msg=name + ' snapshot keys')
self.assertEqual(len(snapshots['0']), 1,
msg=name + ' snapshot length')
state = format_statevector(snapshots['0'][0])
expected_state = expected_data[name]['statevector']
fidelity = np.abs(expected_state.dot(state.conj())) ** 2
self.assertAlmostEqual(fidelity, 1.0, places=10,
msg=name + ' snapshot fidelity')
def get_counts(self, experiment=None):
"""Get the histogram data of an experiment.
Args:
experiment (str or QuantumCircuit or Schedule or int or None): the index of the
experiment, as specified by ``data([experiment])``.
Returns:
dict[str:int] or list[dict[str:int]]: a dictionary or a list of
dictionaries. A dictionary has the counts for each qubit with
the keys containing a string in binary format and separated
according to the registers in circuit (e.g. ``0100 1110``).
The string is little-endian (cr[0] on the right hand side).
Raises:
QiskitError: if there are no counts for the experiment.
"""
if experiment is None:
exp_keys = range(len(self.results))
else:
exp_keys = [experiment]
dict_list = []
for key in exp_keys:
exp = self._get_experiment(key)
try:
header = exp.header.to_dict()
except (AttributeError, QiskitError): # header is not available
header = None
if "counts" in self.data(key).keys():
if header:
counts_header = {
k: v
for k, v in header.items()
if k in {"time_taken", "creg_sizes", "memory_slots"}
}
else:
counts_header = {}
dict_list.append(
Counts(self.data(key)["counts"], **counts_header))
elif "statevector" in self.data(key).keys():
vec = postprocess.format_statevector(
self.data(key)["statevector"])
dict_list.append(
statevector.Statevector(vec).probabilities_dict(
decimals=15))
else:
raise QiskitError('No counts for experiment "{}"'.format(
repr(key)))
# Return first item of dict_list if size is 1
if len(dict_list) == 1:
return dict_list[0]
else:
return dict_list
def test_qobj_two_qubit_gates(self):
filename = self._get_resource_path('qobj/cpp_two_qubit_gates.json')
with open(filename, 'r') as file:
qobj = Qobj.from_dict(json.load(file))
result = self.backend.run(qobj).result()
expected_data = {
'h0 CX01': {
'statevector': np.array([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)])},
'h0 CX10': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0])},
'h1 CX01': {
'statevector': np.array([1 / np.sqrt(2), 0, 1 / np.sqrt(2), 0])},
'h1 CX10': {
'statevector': np.array([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)])},
'h0 cx01': {
'statevector': np.array([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)])},
'h0 cx10': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0])},
'h1 cx01': {
'statevector': np.array([1 / np.sqrt(2), 0, 1 / np.sqrt(2), 0])},
'h1 cx10': {
'statevector': np.array([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)])},
'h0 cz01': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0])},
'h0 cz10': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0])},
'h1 cz01': {
'statevector': np.array([1 / np.sqrt(2), 0, 1 / np.sqrt(2), 0])},
'h1 cz10': {
'statevector': np.array([1 / np.sqrt(2), 0, 1 / np.sqrt(2), 0])},
'h0 h1 cz01': {'statevector': np.array([0.5, 0.5, 0.5, -0.5])},
'h0 h1 cz10': {'statevector': np.array([0.5, 0.5, 0.5, -0.5])},
'h0 rzz01': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2), 0, 0])},
'h0 rzz10': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2), 0, 0])},
'h1 rzz01': {
'statevector': np.array([1 / np.sqrt(2), 0, 1j / np.sqrt(2), 0])},
'h1 rzz10': {
'statevector': np.array([1 / np.sqrt(2), 0, 1j / np.sqrt(2), 0])},
'h0 h1 rzz01': {'statevector': np.array([0.5, 0.5j, 0.5j, 0.5])},
'h0 h1 rzz10': {'statevector': np.array([0.5, 0.5j, 0.5j, 0.5])}
}
for name in expected_data:
# Check snapshot
snapshots = result.data(name)['snapshots']['statevector']
self.assertEqual(set(snapshots), {'0'},
msg=name + ' snapshot keys')
self.assertEqual(len(snapshots['0']), 1,
msg=name + ' snapshot length')
state = format_statevector(snapshots['0'][0])
expected_state = expected_data[name]['statevector']
fidelity = state_fidelity(expected_state, state)
self.assertAlmostEqual(fidelity, 1.0, places=10,
msg=name + ' snapshot fidelity')
def get_counts(self, experiment=None):
"""Get the histogram data of an experiment.
Args:
experiment (str or QuantumCircuit or Schedule or int or None): the index of the
experiment, as specified by ``get_data()``.
Returns:
dict[str:int] or list[dict[str:int]]: a dictionary or a list of
dictionaries. A dictionary has the counts for each qubit with
the keys containing a string in binary format and separated
according to the registers in circuit (e.g. ``0100 1110``).
The string is little-endian (cr[0] on the right hand side).
Raises:
QiskitError: if there are no counts for the experiment.
"""
if experiment is None:
exp_keys = range(len(self.results))
else:
exp_keys = [experiment]
dict_list = []
for key in exp_keys:
exp = self._get_experiment(key)
try:
header = exp.header.to_dict()
except (AttributeError, QiskitError): # header is not available
header = None
if 'counts' in self.data(key).keys():
dict_list.append(postprocess.format_counts(self.data(key)['counts'], header))
elif 'statevector' in self.data(key).keys():
vec = postprocess.format_statevector(self.data(key)['statevector'])
dict_list.append(state_to_counts(vec))
else:
raise QiskitError('No counts for experiment "{0}"'.format(key))
# Return first item of dict_list if size is 1
if len(dict_list) == 1:
return dict_list[0]
else:
return dict_list
def get_statevector(self, experiment=None, decimals=None):
"""Get the final statevector of an experiment.
Args:
experiment (str or QuantumCircuit or Schedule or int or None): the index of the
experiment, as specified by ``data()``.
decimals (int): the number of decimals in the statevector.
If None, does not round.
Returns:
list[complex]: list of 2^num_qubits complex amplitudes.
Raises:
QiskitError: if there is no statevector for the experiment.
"""
try:
return postprocess.format_statevector(self.data(experiment)['statevector'],
decimals=decimals)
except KeyError:
raise QiskitError('No statevector for experiment "{}"'.format(experiment))
def test_snapshot(self):
"""snapshot a bell state in the middle of circuit"""
qr = qiskit.QuantumRegister(2)
cr = qiskit.ClassicalRegister(2)
circuit = qiskit.QuantumCircuit(qr, cr)
circuit.h(qr[0])
circuit.cx(qr[0], qr[1])
circuit.snapshot('3')
circuit.cx(qr[0], qr[1])
circuit.h(qr[1])
sim = Aer.get_backend('statevector_simulator')
result = execute(circuit, sim).result()
# TODO: rely on Result.get_statevector() postprocessing rather than manual
snapshots = result.data(0)['snapshots']['statevector']['3']
snapshot = format_statevector(snapshots[0])
target = [0.70710678 + 0.j, 0. + 0.j, 0. + 0.j, 0.70710678 + 0.j]
fidelity = state_fidelity(snapshot, target)
self.assertGreater(
fidelity, self._desired_fidelity,
"snapshot has low fidelity{0:.2g}.".format(fidelity))
def _format_tree(self, current_node, decimals):
current_node['value'] = postprocess.format_statevector(
current_node['value'], decimals=decimals)
if 'path_0' in current_node:
self._format_tree(current_node['path_0'], decimals)
self._format_tree(current_node['path_1'], decimals)
def test_qobj_measure_opt(self):
filename = self._get_resource_path('qobj/cpp_measure_opt.json')
with open(filename, 'r') as file:
qobj = Qobj.from_dict(json.load(file))
result = self.backend.run(qobj).result()
shots = qobj.config.shots
expected_data = {
'measure (opt)': {
'deterministic': True,
'counts': {'00': shots},
'statevector': np.array([1, 0, 0, 0])},
'x0 measure (opt)': {
'deterministic': True,
'counts': {'01': shots},
'statevector': np.array([0, 1, 0, 0])},
'x1 measure (opt)': {
'deterministic': True,
'counts': {'10': shots},
'statevector': np.array([0, 0, 1, 0])},
'x0 x1 measure (opt)': {
'deterministic': True,
'counts': {'11': shots},
'statevector': np.array([0, 0, 0, 1])},
'y0 measure (opt)': {
'deterministic': True,
'counts': {'01': shots},
'statevector': np.array([0, 1j, 0, 0])},
'y1 measure (opt)': {
'deterministic': True,
'counts': {'10': shots},
'statevector': np.array([0, 0, 1j, 0])},
'y0 y1 measure (opt)': {
'deterministic': True,
'counts': {'11': shots},
'statevector': np.array([0, 0, 0, -1j])},
'h0 measure (opt)': {
'deterministic': False,
'counts': {'00': shots / 2, '01': shots / 2},
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2), 0, 0])},
'h1 measure (opt)': {
'deterministic': False,
'counts': {'00': shots / 2, '10': shots / 2},
'statevector': np.array([1 / np.sqrt(2), 0, 1 / np.sqrt(2), 0])},
'h0 h1 measure (opt)': {
'deterministic': False,
'counts': {'00': shots / 4, '01': shots / 4,
'10': shots / 4, '11': shots / 4},
'statevector': np.array([0.5, 0.5, 0.5, 0.5])},
'bell measure (opt)': {
'deterministic': False,
'counts': {'00': shots / 2, '11': shots / 2},
'statevector': np.array([1 / np.sqrt(2), 0, 0, 1 / np.sqrt(2)])}
}
for name in expected_data:
# Check counts:
counts = result.get_counts(name)
expected_counts = expected_data[name]['counts']
if expected_data[name].get('deterministic', False):
self.assertEqual(counts, expected_counts,
msg=name + ' counts')
else:
threshold = 0.04 * shots
self.assertDictAlmostEqual(counts, expected_counts,
threshold, msg=name + 'counts')
# Check snapshot
snapshots = result.data(name)['snapshots']['statevector']
self.assertEqual(set(snapshots), {'0'},
msg=name + ' snapshot keys')
self.assertEqual(len(snapshots['0']), 1,
msg=name + ' snapshot length')
state = format_statevector(snapshots['0'][0])
expected_state = expected_data[name]['statevector']
fidelity = np.abs(expected_state.dot(state.conj())) ** 2
self.assertAlmostEqual(fidelity, 1.0, places=10,
msg=name + ' snapshot fidelity')
def test_qobj_single_qubit_gates(self):
filename = self._get_resource_path('qobj/cpp_single_qubit_gates.json')
with open(filename, 'r') as file:
qobj = Qobj.from_dict(json.load(file))
result = self.backend.run(qobj).result()
expected_data = {
'snapshot': {
'statevector': np.array([1, 0])},
'id(U)': {
'statevector': np.array([1, 0])},
'id(u3)': {
'statevector': np.array([1, 0])},
'id(u1)': {
'statevector': np.array([1, 0])},
'id(direct)': {
'statevector': np.array([1, 0])},
'x(U)': {
'statevector': np.array([0, 1])},
'x(u3)': {
'statevector': np.array([0, 1])},
'x(direct)': {
'statevector': np.array([0, 1])},
'y(U)': {
'statevector': np.array([0, 1j])},
'y(u3)': {
'statevector': np.array([0, 1j])},
'y(direct)': {
'statevector': np.array([0, 1j])},
'h(U)': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2)])},
'h(u3)': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2)])},
'h(u2)': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2)])},
'h(direct)': {
'statevector': np.array([1 / np.sqrt(2), 1 / np.sqrt(2)])},
'h(direct) z(U)': {
'statevector': np.array([1 / np.sqrt(2), -1 / np.sqrt(2)])},
'h(direct) z(u3)': {
'statevector': np.array([1 / np.sqrt(2), -1 / np.sqrt(2)])},
'h(direct) z(u1)': {
'statevector': np.array([1 / np.sqrt(2), -1 / np.sqrt(2)])},
'h(direct) z(direct)': {
'statevector': np.array([1 / np.sqrt(2), -1 / np.sqrt(2)])},
'h(direct) s(U)': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2)])},
'h(direct) s(u3)': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2)])},
'h(direct) s(u1)': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2)])},
'h(direct) s(direct)': {
'statevector': np.array([1 / np.sqrt(2), 1j / np.sqrt(2)])},
'h(direct) sdg(U)': {
'statevector': np.array([1 / np.sqrt(2), -1j / np.sqrt(2)])},
'h(direct) sdg(u3)': {
'statevector': np.array([1 / np.sqrt(2), -1j / np.sqrt(2)])},
'h(direct) sdg(u1)': {
'statevector': np.array([1 / np.sqrt(2), -1j / np.sqrt(2)])},
'h(direct) sdg(direct)': {
'statevector': np.array([1 / np.sqrt(2), -1j / np.sqrt(2)])},
'h(direct) t(U)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 + 0.5j])},
'h(direct) t(u3)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 + 0.5j])},
'h(direct) t(u1)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 + 0.5j])},
'h(direct) t(direct)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 + 0.5j])},
'h(direct) tdg(U)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 - 0.5j])},
'h(direct) tdg(u3)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 - 0.5j])},
'h(direct) tdg(u1)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 - 0.5j])},
'h(direct) tdg(direct)': {
'statevector': np.array([1 / np.sqrt(2), 0.5 - 0.5j])}
}
for name in expected_data:
# Check snapshot
snapshots = result.data(name)['snapshots']['statevector']
self.assertEqual(set(snapshots), {'0'},
msg=name + ' snapshot keys')
self.assertEqual(len(snapshots['0']), 1,
msg=name + ' snapshot length')
state = format_statevector(snapshots['0'][0])
expected_state = expected_data[name]['statevector']
inner_product = expected_state.dot(state.conj())
self.assertAlmostEqual(inner_product, 1.0, places=10,
msg=name + ' snapshot fidelity')
