def __call__(self, p, *args, **kwargs):
"""
call a wrapped objective.
Parameters
----------
p: numpy array:
Parameters with which to call the objective.
args
kwargs
Returns
-------
numpy.array:
value of self.objective with p translated into variables, as a numpy array.
"""
angles = dict((self.param_keys[i], p[i]) for i in range(self.N))
if self.passive_angles is not None:
angles = {**angles, **self.passive_angles}
vars = format_variable_dictionary(angles)
E = self.objective(variables=vars, samples=self.samples)
if self.print_level > 2:
print("E={:+2.8f}".format(E), " angles=", angles, " samples=",
self.samples)
elif self.print_level > 1:
print("E={:+2.8f}".format(E))
if self.save_history:
self.history.append(E)
self.history_angles.append(angles)
return numpy.float64(E) # jax types confuses optimizers
def simulate(objective: typing.Union['Objective', 'QCircuit'],
variables: Dict[Union[Variable, Hashable], RealNumber] = None,
samples: int = None,
backend: str = None,
noise: NoiseModel = None,
device: str = None,
*args,
**kwargs) -> Union[RealNumber, 'QubitWaveFunction']:
"""Simulate a tequila objective or circuit
Parameters
----------
objective: Objective:
tequila objective or circuit
variables: Dict:
The variables of the objective given as dictionary
with keys as tequila Variables/hashable types and values the corresponding real numbers
samples : int, optional:
if None a full wavefunction simulation is performed, otherwise a fixed number of samples is simulated
backend : str, optional:
specify the backend or give None for automatic assignment
noise: NoiseModel, optional:
specify a noise model to apply to simulation/sampling
device:
a device upon which (or in emulation of which) to sample
*args :
**kwargs :
read_out_qubits = list[int] (define the qubits which shall be measured, has only effect on pure QCircuit simulation with samples)
Returns
-------
float or QubitWaveFunction
the result of simulation.
"""
variables = format_variable_dictionary(variables)
if variables is None and not (len(objective.extract_variables()) == 0):
raise TequilaException(
"You called simulate for a parametrized type but forgot to pass down the variables: {}"
.format(objective.extract_variables()))
compiled_objective = compile(objective=objective,
samples=samples,
variables=variables,
backend=backend,
noise=noise,
device=device,
*args,
**kwargs)
return compiled_objective(variables=variables,
samples=samples,
*args,
**kwargs)
def simulate(objective: typing.Union['Objective', 'QCircuit'],
variables: Dict[Union[Variable, Hashable], RealNumber] = None,
samples: int = None,
backend: str = None,
noise_model=None,
*args,
**kwargs) -> Union[RealNumber, 'QubitWaveFunction']:
"""Simulate a tequila objective or circuit
Parameters
----------
objective :
tequila objective or circuit
variables :
The variables of the objective given as dictionary
with keys as tequila Variables/hashable types and values the corresponding real numbers
samples : int : (Default value = None)
if None a full wavefunction simulation is performed, otherwise a fixed number of samples is simulated
backend : str : (Default value = None)
specify the backend or give None for automatic assignment
noise_model: NoiseModel :
specify a noise model to apply to simulation/sampling
*args :
**kwargs :
Returns
-------
type
simulated/sampled objective or simulated/sampled wavefunction
"""
variables = format_variable_dictionary(variables)
if variables is None and not (len(objective.extract_variables()) == 0):
raise TequilaException(
"You called simulate for a parametrized type but forgot to pass down the variables: {}"
.format(objective.extract_variables()))
compiled_objective = compile(objective=objective,
samples=samples,
variables=variables,
backend=backend,
noise_model=noise_model,
*args,
**kwargs)
return compiled_objective(variables=variables,
samples=samples,
*args,
**kwargs)
def draw(objective, variables=None, backend: str = None):
"""
Pretty output (depends on installed backends)
Parameters
----------
objective :
the tequila objective to print out
variables : optional:
Give variables if the objective is parametrized (not necesarry for displaying)
backend: str, optional:
chose preferred backend (of None or not found it will be automatically picked)
"""
if backend not in INSTALLED_SIMULATORS:
backend = None
if backend is None:
if "cirq" in INSTALLED_SIMULATORS:
backend = "cirq"
elif "qiskit" in INSTALLED_SIMULATORS:
backend = "qiskit"
if isinstance(objective, Objective) or isinstance(objective,
VectorObjective):
print(objective)
drawn = {}
for i, E in enumerate(objective.get_expectationvalues()):
if E in drawn:
print("\nExpectation Value {} is the same as {}".format(
i, drawn[E]))
else:
print("\nExpectation Value {}".format(i))
print("Hamiltonian : ", E.H)
print("variables : ", E.U.extract_variables())
print("circuit:\n")
draw(E.U, backend=backend)
drawn[E] = i
else:
if backend is None:
print(objective)
else:
if variables is None:
variables = {}
for k in objective.extract_variables():
if k not in variables:
variables[k] = 0.0
variables = format_variable_dictionary(variables)
compiled = compile_circuit(abstract_circuit=objective,
backend=backend,
variables=variables)
print(compiled.circuit)
def __call__(self, p, *args, **kwargs):
angles = dict((self.param_keys[i], p[i]) for i in range(self.N))
if self.passive_angles is not None:
angles = {**angles, **self.passive_angles}
vars = format_variable_dictionary(angles)
E = self.objective(variables=vars,
samples=self.samples,
**self.backend_options)
if not self.silent:
print("E=", E, " angles=", angles, " samples=", self.samples)
if self.save_history:
self.history.append(E)
self.history_angles.append(angles)
return numpy.float64(E) # jax types confuses optimizers