def list_quantum_computers(connection: ForestConnection = None,
qpus=True,
qvms=True) -> List[str]:
"""
List the names of available quantum computers
:param connection: An optional :py:class:ForestConnection` object. If not specified,
the default values for URL endpoints will be used, and your API key
will be read from ~/.pyquil_config. If you deign to change any
of these parameters, pass your own :py:class:`ForestConnection` object.
:param qpus: Whether to include QPU's in the list.
:param qvms: Whether to include QVM's in the list.
"""
if connection is None:
# TODO: Use this to list devices?
connection = ForestConnection()
qc_names = []
if qpus:
# TODO: add deployed QPUs from web endpoint
pass
if qvms:
qc_names += ['9q-generic-qvm', '9q-generic-noisy-qvm']
return qc_names
def _get_qvm_qc(name: str,
qvm_type: str,
device: AbstractDevice,
noise_model: NoiseModel = None,
requires_executable: bool = False,
connection: ForestConnection = None) -> QuantumComputer:
"""Construct a QuantumComputer backed by a QVM.
This is a minimal wrapper over the QuantumComputer, QVM, and QVMCompiler constructors.
:param name: A string identifying this particular quantum computer.
:param qvm_type: The type of QVM. Either qvm or pyqvm.
:param device: A device following the AbstractDevice interface.
:param noise_model: An optional noise model
:param requires_executable: Whether this QVM will refuse to run a :py:class:`Program` and
only accept the result of :py:func:`compiler.native_quil_to_executable`. Setting this
to True better emulates the behavior of a QPU.
:param connection: An optional :py:class:`ForestConnection` object. If not specified,
the default values for URL endpoints will be used.
:return: A QuantumComputer backed by a QVM with the above options.
"""
if connection is None:
connection = ForestConnection()
return QuantumComputer(
name=name,
qam=_get_qvm_or_pyqvm(qvm_type=qvm_type,
connection=connection,
noise_model=noise_model,
device=device,
requires_executable=requires_executable),
device=device,
compiler=QVMCompiler(device=device,
endpoint=connection.compiler_endpoint))
def list_quantum_computers(connection: ForestConnection = None,
qpus: bool = True,
qvms: bool = True) -> List[str]:
"""
List the names of available quantum computers
:param connection: An optional :py:class:ForestConnection` object. If not specified,
the default values for URL endpoints will be used, and your API key
will be read from ~/.pyquil_config. If you deign to change any
of these parameters, pass your own :py:class:`ForestConnection` object.
:param qpus: Whether to include QPU's in the list.
:param qvms: Whether to include QVM's in the list.
"""
if connection is None:
connection = ForestConnection()
qc_names: List[str] = []
if qpus:
qc_names += list(list_lattices(connection=connection).keys())
if qvms:
qc_names += ['9q-square-qvm', '9q-square-noisy-qvm']
return qc_names
def get_qc(name: str,
*,
as_qvm: bool = None,
noisy: bool = None,
connection: ForestConnection = None) -> QuantumComputer:
"""
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("Aspen-0-12Q-A-noisy-qvm")
>>> qc = get_qc("Aspen-0-12Q-A", 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-square-qvm")
>>> qc = get_qc("9q-square", as_qvm=True)
Finally, you can get request a QVM with "no" topology of a given number of qubits
(technically, it's a fully connected graph among the given number of qubits) with::
>>> qc = get_qc("5q-qvm") # or "6q-qvm", or "34q-qvm", ...
Redundant flags are acceptable, but conflicting flags will raise an exception::
>>> qc = get_qc("9q-square-qvm") # qc is fully specified by its name
>>> qc = get_qc("9q-square-qvm", as_qvm=True) # redundant, but ok
>>> qc = get_qc("9q-square-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()
full_name = name
name, as_qvm, noisy = _parse_name(name, as_qvm, noisy)
ma = re.fullmatch(r'(\d+)q', name)
if ma is not None:
n_qubits = int(ma.group(1))
if not as_qvm:
raise ValueError("Please name a valid device or run as a QVM")
return _get_unrestricted_qvm(connection=connection,
noisy=noisy,
n_qubits=n_qubits)
if name == '9q-generic' or name == '9q-square':
if name == '9q-generic':
warnings.warn("Please prefer '9q-square' instead of '9q-generic'",
DeprecationWarning)
if not as_qvm:
raise ValueError(
"The device '9q-square' is only available as a QVM")
return _get_9q_square_qvm(connection=connection, noisy=noisy)
device = get_lattice(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 controlling noise models on QVMs.")
return QuantumComputer(name=full_name,
qam=QPU(endpoint=pyquil_config.qpu_url,
user=pyquil_config.user_id),
device=device,
compiler=QPUCompiler(
endpoint=pyquil_config.compiler_url,
device=device))
if noisy:
noise_model = device.noise_model
else:
noise_model = None
return QuantumComputer(name=full_name,
qam=QVM(connection=connection,
noise_model=noise_model),
device=device,
compiler=_get_qvm_compiler_based_on_endpoint(
device=device,
endpoint=connection.compiler_endpoint))
def __init__(self,
clauses,
m=None,
steps=1,
grid_size=None,
tol=1e-5,
gate_noise=None,
verbose=False,
visualize=False):
self.clauses = clauses
self.m = m
self.verbose = verbose
self.visualize = visualize
self.step_by_step_results = None
self.optimization_history = None
self.gate_noise = gate_noise
if grid_size is None:
self.grid_size = len(clauses) + len(qubits)
else:
self.grid_size = grid_size
cost_operators, mapping = self.create_operators_from_clauses()
self.mapping = mapping
driver_operators = self.create_driver_operators()
# minimizer_kwargs = {'method': 'BFGS',
# 'options': {'gtol': tol, 'disp': False}}
# bounds = [(0, np.pi)]*steps + [(0, 2*np.pi)]*steps
# minimizer_kwargs = {'method': 'L-BFGS-B',
# 'options': {'gtol': tol, 'disp': False},
# 'bounds': bounds}
minimizer_kwargs = {
'method': 'Nelder-Mead',
'options': {
'ftol': tol,
'tol': tol,
'disp': False
}
}
if self.verbose:
print_fun = print
else:
print_fun = pass_fun
qubits = list(range(len(mapping)))
if gate_noise:
self.samples = int(1e3)
pauli_channel = [gate_noise] * 3
else:
self.samples = None
pauli_channel = None
connection = ForestConnection()
qvm = QVM(connection=connection, gate_noise=pauli_channel)
topology = nx.complete_graph(len(qubits))
device = NxDevice(topology=topology)
qc = QuantumComputer(name="my_qvm",
qam=qvm,
device=device,
compiler=QVMCompiler(
device=device,
endpoint=connection.compiler_endpoint))
vqe_option = {
'disp': print_fun,
'return_all': True,
'samples': self.samples
}
self.qaoa_inst = QAOA(qc,
qubits,
steps=steps,
init_betas=None,
init_gammas=None,
cost_ham=cost_operators,
ref_ham=driver_operators,
minimizer=scipy.optimize.minimize,
minimizer_kwargs=minimizer_kwargs,
rand_seed=None,
vqe_options=vqe_option,
store_basis=True)
self.ax = None
