def minimize(objective: Objective,
gradient: typing.Union[str, typing.Dict[Variable, Objective]] = None,
hessian: typing.Union[str, typing.Dict[typing.Tuple[Variable, Variable], Objective]] = None,
qng: bool = None,
initial_values: typing.Dict[typing.Hashable, numbers.Real] = None,
variables: typing.List[typing.Hashable] = None,
samples: int = None,
maxiter: int = 100,
backend: str = None,
backend_options: dict = None,
noise: NoiseModel = None,
method: str = "BFGS",
tol: float = 1.e-3,
method_options: dict = None,
method_bounds: typing.Dict[typing.Hashable, numbers.Real] = None,
method_constraints=None,
silent: bool = False,
save_history: bool = True,
*args,
**kwargs) -> SciPyReturnType:
"""
Parameters
----------
objective: Objective :
The tequila objective to optimize
gradient: typing.Union[str, typing.Dict[Variable, Objective], None] : (Default value = None) :
'2-point', 'cs' or '3-point' for numerical gradient evaluation (does not work in combination with all optimizers),
dictionary of variables and tequila objective to define own gradient,
None for automatic construction (default)
hessian: typing.Union[str, typing.Dict[Variable, Objective], None] : (Default value = None) :
'2-point', 'cs' or '3-point' for numerical gradient evaluation (does not work in combination with all optimizers),
dictionary (keys:tuple of variables, values:tequila objective) to define own gradient,
None for automatic construction (default)
qng: bool : (Default value = False) :
whether or not, in the event that a gradient-based method is to be used, the qng, rather than the standard gradient,
should be employed. NOTE: throws an error for anything but a single expectationvalue with no passive angles.
initial_values: typing.Dict[typing.Hashable, numbers.Real]: (Default value = None):
Initial values as dictionary of Hashable types (variable keys) and floating point numbers. If given None they will all be set to zero
variables: typing.List[typing.Hashable] :
(Default value = None)
List of Variables to optimize
samples: int :
(Default value = None)
samples/shots to take in every run of the quantum circuits (None activates full wavefunction simulation)
maxiter: int :
(Default value = 100)
backend: str :
(Default value = None)
Simulator backend, will be automatically chosen if set to None
backend_options: dict:
(Default value = None)
Additional options for the backend
Will be unpacked and passed to the compiled objective in every call
noise: NoiseModel:
(Default value =None)
a NoiseModel to apply to all expectation values in the objective.
method: str :
(Default value = "BFGS")
Optimization method (see scipy documentation, or 'available methods')
tol: float :
(Default value = 1.e-3)
Convergence tolerance for optimization (see scipy documentation)
method_options: dict :
(Default value = None)
Dictionary of options
(see scipy documentation)
method_bounds: typing.Dict[typing.Hashable, typing.Tuple[float, float]]:
(Default value = None)
bounds for the variables (see scipy documentation)
method_constraints :
(Default value = None)
(see scipy documentation
silent: bool :
(Default value = False)
No printout if True
save_history: bool:
(Default value = True)
Save the history throughout the optimization
Returns
-------
"""
# bring into right format
variables = format_variable_list(variables)
initial_values = format_variable_dictionary(initial_values)
if isinstance(gradient, dict) or hasattr(gradient, "items"):
gradient = format_variable_dictionary(gradient)
if isinstance(hessian, dict) or hasattr(hessian, "items"):
hessian = {(assign_variable(k[0]), assign_variable([k[1]])): v for k, v in hessian.items()}
method_bounds = format_variable_dictionary(method_bounds)
# set defaults
all_variables = objective.extract_variables()
if variables is None:
variables = all_variables
if initial_values is None:
initial_values = {k: numpy.random.uniform(0, 2 * numpy.pi) for k in all_variables}
else:
# autocomplete initial values, warn if you did
detected = False
for k in all_variables:
if k not in initial_values:
initial_values[k] = numpy.random.uniform(0, 2 * numpy.pi)
detected = True
if detected and not silent:
print("WARNING: initial_variables given but not complete: Autocomplete with random number")
optimizer = OptimizerSciPy(save_history=save_history,
maxiter=maxiter,
method=method,
method_options=method_options,
method_bounds=method_bounds,
method_constraints=method_constraints,
silent=silent,
tol=tol)
if initial_values is not None:
initial_values = {assign_variable(k): v for k, v in initial_values.items()}
return optimizer(objective=objective, qng=qng,
backend=backend, backend_options=backend_options, gradient=gradient, hessian=hessian, initial_values=initial_values,
variables=variables, noise=noise,
samples=samples, *args, **kwargs)
def minimize(objective: Objective,
lr=0.01,
method='sgd',
qng: bool = False,
stop_count=None,
initial_values: typing.Dict[typing.Hashable, numbers.Real] = None,
variables: typing.List[typing.Hashable] = None,
samples: int = None,
maxiter: int = 100,
backend: str = None,
noise: NoiseModel = None,
silent: bool = False,
save_history: bool = True,
*args,
**kwargs) -> GDReturnType:
"""
Parameters
----------
objective: Objective :
The tequila objective to optimize
lr: float >0:
the learning rate. Default 0.01.
method: string:
which variation on Gradient Descent to use. Options include 'sgd','adam','nesterov','adagrad','rmsprop',
qng: bool:
whether or not the gradient calculated should be the quantum natural gradient or not. defaults to False.
stop_count: int:
how many steps after which to cease training if no improvement occurs. Default None results in going till maxiter is complete
initial_values: typing.Dict[typing.Hashable, numbers.Real]: (Default value = None):
Initial values as dictionary of Hashable types (variable keys) and floating point numbers. If given None they will all be set to zero
variables: typing.List[typing.Hashable] :
(Default value = None)
List of Variables to optimize
samples: int :
(Default value = None)
samples/shots to take in every run of the quantum circuits (None activates full wavefunction simulation)
maxiter: int :
(Default value = 100)
backend: str :
(Default value = None)
Simulator backend, will be automatically chosen if set to None
noise: NoiseModel:
(Default value = None)
a NoiseModel to apply to all expectation values in the objective.
stop_count: int :
(Default value = None)
Convergence tolerance for optimization; if no improvement after this many epochs, stop.
silent: bool :
(Default value = False)
No printout if True
save_history: bool:
(Default value = True)
Save the history throughout the optimization
optional kwargs may include beta, beta2, and rho, parameters which affect (but do not need to be altered) the various
method algorithms.
Returns
-------
"""
# bring into right format
variables = format_variable_list(variables)
initial_values = format_variable_dictionary(initial_values)
# set defaults
all_variables = objective.extract_variables()
if variables is None:
variables = all_variables
if initial_values is None:
initial_values = {
k: numpy.random.uniform(0, 2 * numpy.pi)
for k in all_variables
}
else:
# autocomplete initial values, warn if you did
detected = False
for k in all_variables:
if k not in initial_values:
initial_values[k] = numpy.random.uniform(0, 2 * numpy.pi)
detected = True
if detected and not silent:
print(
"WARNING: initial_variables given but not complete: Autocomplete with random number"
)
optimizer = OptimizerGD(save_history=save_history,
maxiter=maxiter,
silent=silent)
if initial_values is not None:
initial_values = {
assign_variable(k): v
for k, v in initial_values.items()
}
return optimizer(objective=objective,
maxiter=maxiter,
lr=lr,
method=method,
qng=qng,
stop_count=stop_count,
backend=backend,
initial_values=initial_values,
variables=variables,
noise=noise,
samples=samples,
*args,
**kwargs)