def test_reset(forest):
device = NxDevice(nx.complete_graph(3))
qc = QuantumComputer(name="testy!",
qam=QVM(connection=forest),
device=device,
compiler=DummyCompiler())
p = Program(
Declare(name="theta", memory_type="REAL"),
Declare(name="ro", memory_type="BIT"),
RX(MemoryReference("theta"), 0),
MEASURE(0, MemoryReference("ro")),
).wrap_in_numshots_loop(1000)
qc.run(executable=p, memory_map={"theta": [np.pi]})
aref = ParameterAref(name="theta", index=0)
assert qc.qam._variables_shim[aref] == np.pi
assert qc.qam._executable == p
assert qc.qam._memory_results["ro"].shape == (1000, 1)
assert all([bit == 1 for bit in qc.qam._memory_results["ro"]])
assert qc.qam.status == "done"
qc.reset()
assert qc.qam._variables_shim == {}
assert qc.qam._executable is None
assert qc.qam._memory_results["ro"] is None
assert qc.qam.status == "connected"
def test_measure_bitstrings(forest):
device = NxDevice(nx.complete_graph(2))
qc_pyqvm = QuantumComputer(name="testy!",
qam=PyQVM(n_qubits=2),
device=device,
compiler=DummyCompiler())
qc_forest = QuantumComputer(
name="testy!",
qam=QVM(connection=forest, gate_noise=[0.00] * 3),
device=device,
compiler=DummyCompiler(),
)
prog = Program(I(0), I(1))
meas_qubits = [0, 1]
sym_progs, flip_array = _symmetrization(prog, meas_qubits, symm_type=-1)
results = _measure_bitstrings(qc_pyqvm,
sym_progs,
meas_qubits,
num_shots=1)
# test with pyQVM
answer = [
np.array([[0, 0]]),
np.array([[0, 1]]),
np.array([[1, 0]]),
np.array([[1, 1]])
]
assert all([np.allclose(x, y) for x, y in zip(results, answer)])
# test with regular QVM
results = _measure_bitstrings(qc_forest,
sym_progs,
meas_qubits,
num_shots=1)
assert all([np.allclose(x, y) for x, y in zip(results, answer)])
def compile_rigetti(num_qubits, topology, program):
if topology.lower() == 'ring':
edge_list = []
for i in range(0, num_qubits):
edge_list.append((i, (i + 1) % num_qubits))
topology = nx.from_edgelist(edge_list)
device = NxDevice(topology)
compiler = LocalQVMCompiler("http://localhost:6000", device)
my_qc = QuantumComputer(name='my_qc',
qam=QVM(connection=ForestConnection()),
device=device,
compiler=compiler)
executable = compiler.quil_to_native_quil(
program) #create QC compatible specification
depth = compute_depth_rigetti(executable)
volume = len(executable) - 3 #subtract extra directives
print(executable)
q2_count = two_qubit_count(executable)
out_str = str(executable)
out_str = out_str + ("#DEPTH: %s |VOL.: %s |2Q GATE COUNT: %s\n" %
(depth, volume, q2_count))
print("DEPTH: %s |VOL.: %s |2Q GATE COUNT: %s" %
(depth, volume, q2_count))
print()
print()
return out_str
def test_qc():
from pyquil.api import ForestConnection, QuantumComputer
from pyquil.api._compiler import _extract_attribute_dictionary_from_program
from pyquil.api._qac import AbstractCompiler
from pyquil.device import NxDevice
from pyquil.gates import I
class BasicQVMCompiler(AbstractCompiler):
def quil_to_native_quil(self, program: Program):
return basic_compile(program)
def native_quil_to_executable(self, nq_program: Program):
return PyQuilExecutableResponse(
program=nq_program.out(),
attributes=_extract_attribute_dictionary_from_program(
nq_program))
try:
qc = QuantumComputer(
name='testing-qc',
qam=QVM(connection=ForestConnection(), random_seed=52),
device=NxDevice(nx.complete_graph(2)),
compiler=BasicQVMCompiler(),
)
qc.run_and_measure(Program(I(0)), trials=1)
return qc
except (RequestException, TimeoutError) as e:
return pytest.skip(
"This test requires a running local QVM: {}".format(e))
def test_readout_symmetrization(forest):
device = NxDevice(nx.complete_graph(3))
noise_model = decoherence_noise_with_asymmetric_ro(
gates=gates_in_isa(device.get_isa()))
qc = QuantumComputer(
name="testy!",
qam=QVM(connection=forest, noise_model=noise_model),
device=device,
compiler=DummyCompiler(),
)
prog = Program(I(0), X(1), MEASURE(0, MemoryReference("ro", 0)),
MEASURE(1, MemoryReference("ro", 1)))
prog.wrap_in_numshots_loop(1000)
bs1 = qc.run(prog)
avg0_us = np.mean(bs1[:, 0])
avg1_us = 1 - np.mean(bs1[:, 1])
diff_us = avg1_us - avg0_us
assert diff_us > 0.03
bs2 = qc.run_symmetrized_readout(prog, 1000)
avg0_s = np.mean(bs2[:, 0])
avg1_s = 1 - np.mean(bs2[:, 1])
diff_s = avg1_s - avg0_s
assert diff_s < 0.05
def test_qvm_version(forest: ForestConnection):
qvm = QVM(connection=forest)
version_info = qvm.get_version_info()
assert isinstance(version_info, dict)
assert 'qvm-app' in version_info
assert 'qvm-lib' in version_info
def is_a_version_string(version_string: str):
parts = version_string.split('.')
try:
map(int, parts)
except ValueError:
return False
return True
assert is_a_version_string(version_info['qvm-app'])
assert is_a_version_string(version_info['qvm-lib'])
def test_run(forest):
device = NxDevice(nx.complete_graph(3))
qc = QuantumComputer(name='testy!',
qam=QVM(connection=forest, gate_noise=[0.01] * 3),
device=device,
compiler=DummyCompiler())
bitstrings = qc.run(
Program(H(0), CNOT(0, 1), CNOT(1, 2), MEASURE(0, 0), MEASURE(1, 1),
MEASURE(2, 2)).wrap_in_numshots_loop(1000))
assert bitstrings.shape == (1000, 3)
parity = np.sum(bitstrings, axis=1) % 3
assert 0 < np.mean(parity) < 0.15
def test_run_with_parameters(forest):
device = NxDevice(nx.complete_graph(3))
qc = QuantumComputer(name='testy!',
qam=QVM(connection=forest),
device=device,
compiler=DummyCompiler())
bitstrings = qc.run(executable=Program(
Declare(name='theta', memory_type='REAL'),
Declare(name='ro', memory_type='BIT'), RX(MemoryReference('theta'), 0),
MEASURE(0, MemoryReference('ro'))).wrap_in_numshots_loop(1000),
memory_map={'theta': [np.pi]})
assert bitstrings.shape == (1000, 1)
assert all([bit == 1 for bit in bitstrings])
def test_run_with_parameters(forest):
device = NxDevice(nx.complete_graph(3))
qc = QuantumComputer(
name="testy!", qam=QVM(connection=forest), device=device, compiler=DummyCompiler()
)
bitstrings = qc.run(
executable=Program(
Declare(name="theta", memory_type="REAL"),
Declare(name="ro", memory_type="BIT"),
RX(MemoryReference("theta"), 0),
MEASURE(0, MemoryReference("ro")),
).wrap_in_numshots_loop(1000),
memory_map={"theta": [np.pi]},
)
assert bitstrings.shape == (1000, 1)
assert all([bit == 1 for bit in bitstrings])
def test_qc_expectation_larger_lattice(forest):
device = NxDevice(nx.complete_graph(4))
qc = QuantumComputer(name='testy!',
qam=QVM(connection=forest),
device=device,
compiler=DummyCompiler())
q0 = 2
q1 = 3
# bell state program
p = Program()
p += RESET()
p += H(q0)
p += CNOT(q0, q1)
p.wrap_in_numshots_loop(10)
# XX, YY, ZZ experiment
sx = ExperimentSetting(in_state=sZ(q0) * sZ(q1),
out_operator=sX(q0) * sX(q1))
sy = ExperimentSetting(in_state=sZ(q0) * sZ(q1),
out_operator=sY(q0) * sY(q1))
sz = ExperimentSetting(in_state=sZ(q0) * sZ(q1),
out_operator=sZ(q0) * sZ(q1))
e = TomographyExperiment(settings=[sx, sy, sz], program=p)
results = qc.experiment(e)
# XX expectation value for bell state |00> + |11> is 1
assert np.isclose(results[0].expectation, 1)
assert np.isclose(results[0].std_err, 0)
assert results[0].total_counts == 40
# YY expectation value for bell state |00> + |11> is -1
assert np.isclose(results[1].expectation, -1)
assert np.isclose(results[1].std_err, 0)
assert results[1].total_counts == 40
# ZZ expectation value for bell state |00> + |11> is 1
assert np.isclose(results[2].expectation, 1)
assert np.isclose(results[2].std_err, 0)
assert results[2].total_counts == 40
def test_run(forest):
qc = QuantumComputer(
name='testy!',
qam=QVM(connection=forest, gate_noise=[0.01] * 3),
device=NxDevice(nx.complete_graph(3)),
)
bitstrings = qc.run(Program(
H(0),
CNOT(0, 1),
CNOT(1, 2),
MEASURE(0, 0),
MEASURE(1, 1),
MEASURE(2, 2),
),
classical_addresses=[0, 1, 2],
trials=1000)
assert bitstrings.shape == (1000, 3)
parity = np.sum(bitstrings, axis=1) % 3
assert 0 < np.mean(parity) < 0.15
def test_readout_symmetrization(forest):
device = NxDevice(nx.complete_graph(3))
noise_model = decoherance_noise_with_asymmetric_ro(device.get_isa())
qc = QuantumComputer(name='testy!',
qam=QVM(connection=forest, noise_model=noise_model),
device=device)
prog = Program(I(0), X(1), MEASURE(0, 0), MEASURE(1, 1))
bs1 = qc.run(prog, [0, 1], 1000)
avg0_us = np.mean(bs1[:, 0])
avg1_us = 1 - np.mean(bs1[:, 1])
diff_us = avg1_us - avg0_us
assert diff_us > 0.03
bs2 = qc.run_symmetrized_readout(prog, [0, 1], 1000)
avg0_s = np.mean(bs2[:, 0])
avg1_s = 1 - np.mean(bs2[:, 1])
diff_s = avg1_s - avg0_s
assert diff_s < 0.05
def test_qc_expectation_on_qvm_that_requires_executable(forest):
# regression test for https://github.com/rigetti/forest-tutorials/issues/2
device = NxDevice(nx.complete_graph(2))
qc = QuantumComputer(
name="testy!",
qam=QVM(connection=forest, requires_executable=True),
device=device,
compiler=DummyCompiler(),
)
p = Program()
theta = p.declare("theta", "REAL")
p += RESET()
p += RY(theta, 0)
p.wrap_in_numshots_loop(10000)
sx = ExperimentSetting(in_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
topology = nx.from_edgelist([
(10, 2),
(10, 4),
(10, 6),
(10, 8),
])
device = NxDevice(topology)
class MyLazyCompiler(AbstractCompiler):
def quil_to_native_quil(self, program, *, protoquil=None):
return program
def native_quil_to_executable(self, nq_program):
return nq_program
my_qc = QuantumComputer(
name='my-qvm',
qam=QVM(connection=ForestConnection()),
device=device,
compiler=MyLazyCompiler(),
)
nx.draw(my_qc.qubit_topology())
plt.title('5qcm', fontsize=18)
plt.show()
my_qc.run_and_measure(Program(X(10)), trials=5)
def test_qvm_run_only_pqer(forest: ForestConnection):
qvm = QVM(connection=forest, gate_noise=[0.01] * 3, requires_executable=True)
p = Program(Declare("ro", "BIT"), X(0), MEASURE(0, MemoryReference("ro")))
p.wrap_in_numshots_loop(1000)
with pytest.raises(TypeError) as e:
qvm.load(p)
qvm.run()
qvm.wait()
assert e.match(r".*Make sure you have explicitly compiled your program.*")
nq = PyQuilExecutableResponse(program=p.out(), attributes={"num_shots": 1000})
qvm.load(nq)
qvm.run()
qvm.wait()
bitstrings = qvm.read_memory(region_name="ro")
assert bitstrings.shape == (1000, 1)
assert np.mean(bitstrings) > 0.8
def test_qvm_run_region_not_declared_not_measured_non_ro(forest: ForestConnection):
qvm = QVM(connection=forest)
p = Program(X(0))
nq = PyQuilExecutableResponse(program=p.out(), attributes={"num_shots": 100})
qvm.load(nq).run().wait()
assert qvm.read_memory(region_name="reg") is None
def test_qvm_run_region_not_declared_not_measured(
client_configuration: QCSClientConfiguration):
qvm = QVM(client_configuration=client_configuration)
p = Program(X(0))
result = qvm.run(p.wrap_in_numshots_loop(100))
assert result.readout_data.get("ro") is None
def test_qvm__default_client(client_configuration: QCSClientConfiguration):
qvm = QVM(client_configuration=client_configuration)
p = Program(Declare("ro", "BIT"), X(0), MEASURE(0, MemoryReference("ro")))
result = qvm.run(p.wrap_in_numshots_loop(1000))
bitstrings = result.readout_data.get("ro")
assert bitstrings.shape == (1000, 1)
def get_qc(name: str,
*,
as_qvm: bool = None,
noisy: bool = None,
connection: ForestConnection = None):
"""
Get a quantum computer.
A quantum computer is an object of type :py:class:`QuantumComputer` and can be backed
either by a QVM simulator ("Quantum/Quil Virtual Machine") or a physical Rigetti QPU ("Quantum
Processing Unit") made of superconducting qubits.
You can choose the quantum computer to target through a combination of its name and optional
flags. There are multiple ways to get the same quantum computer. The following are equivalent::
>>> qc = get_qc("8Q-Agave-noisy-qvm")
>>> qc = get_qc("8Q-Agave", as_qvm=True, noisy=True)
and will construct a simulator of the 8q-agave chip with a noise model based on device
characteristics. We also provide a means for constructing generic quantum simulators that
are not related to a given piece of Rigetti hardware::
>>> qc = get_qc("9q-generic-qvm")
>>> qc = get_qc("9q-generic", as_qvm=True)
Redundant flags are acceptable, but conflicting flags will raise an exception::
>>> qc = get_qc("9q-generic-qvm") # qc is fully specified by its name
>>> qc = get_qc("9q-generic-qvm", as_qvm=True) # redundant, but ok
>>> qc = get_qc("9q-generic-qvm", as_qvm=False) # Error!
Use :py:func:`list_quantum_computers` to retrieve a list of known qc names.
This method is provided as a convenience to quickly construct and use QVM's and QPU's.
Power users may wish to have more control over the specification of a quantum computer
(e.g. custom noise models, bespoke topologies, etc.). This is possible by constructing
a :py:class:`QuantumComputer` object by hand. Please refer to the documentation on
:py:class:`QuantumComputer` for more information.
:param name: The name of the desired quantum computer. This should correspond to a name
returned by :py:func:`list_quantum_computers`. Names ending in "-qvm" will return
a QVM. Names ending in "-noisy-qvm" will return a QVM with a noise model. Otherwise,
we will return a QPU with the given name.
:param as_qvm: An optional flag to force construction of a QVM (instead of a QPU). If
specified and set to ``True``, a QVM-backed quantum computer will be returned regardless
of the name's suffix
:param noisy: An optional flag to force inclusion of a noise model. If
specified and set to ``True``, a quantum computer with a noise model will be returned
regardless of the name's suffix. The noise model for QVM's based on a real QPU
is an empirically parameterized model based on real device noise characteristics.
The generic QVM noise model is simple T1 and T2 noise plus readout error. See
:py:func:`decoherance_noise_with_asymmetric_ro`.
:param connection: An optional :py:class:ForestConnection` object. If not specified,
the default values for URL endpoints, ping time, and status time will be used. Your
user id and API key will be read from ~/.pyquil_config. If you deign to change any
of these parameters, pass your own :py:class:`ForestConnection` object.
:return:
"""
if connection is None:
connection = ForestConnection()
name, as_qvm, noisy = _parse_name(name, as_qvm, noisy)
if name == '9q-generic':
if not as_qvm:
raise ValueError(
"The device '9q-generic' is only available as a QVM")
nineq_square = nx.convert_node_labels_to_integers(
nx.grid_2d_graph(3, 3))
nineq_device = NxDevice(topology=nineq_square)
if noisy:
noise_model = decoherance_noise_with_asymmetric_ro(
nineq_device.get_isa())
else:
noise_model = None
return QuantumComputer(name='9q-generic-qvm',
qam=QVM(connection=connection,
noise_model=noise_model),
device=nineq_device)
# At least based off a real device.
device = get_devices(as_dict=True)[name]
if not as_qvm:
if noisy is not None and noisy:
warnings.warn(
"You have specified `noisy=True`, but you're getting a QPU. This flag "
"is meant for controling noise models on QVMs.")
return QuantumComputer(name=name,
qam=QPU(device_name=name,
connection=connection),
device=device)
if noisy:
noise_model = device.noise_model
name = "{name}-noisy-qvm".format(name=name)
else:
noise_model = None
name = "{name}-qvm".format(name=name)
return QuantumComputer(name=name,
qam=QVM(connection=connection,
noise_model=noise_model),
device=device)
