def get_qng_combos(objective,
func=stokes_block,
initial_values=None,
samples=None,
backend=None,
device=None,
noise=None) -> typing.List[typing.Dict]:
"""
get all the objects needed to evaluate the qng for some objective; return them in a list of dictionaries.
Parameters
----------
objective: Objective:
the Objective whose qng is sought.
func: callable: (Default = stokes_block):
the function used to obtain the (blocks of) the qgt. Default uses stokes_block, defined above.
initial_values: dict, optional:
a dictionary indicating the intial parameters with which to compile all objectives appearing in the qng.
samples: int, optional:
the number of samples with which to compile all objectives appearing in the qng. Default: none.
backend: str, optional:
the backend with which to compile all objectives appearing in the qng. default: pick for you.
device: optional:
the device with which to compile all objectives appearing in the qng. Default: no device use or emulation.
noise: str or NoiseModel, optional:
the noise model with which to compile all objectives appearing in the qng. Default: no noise.
Returns
-------
list of dicts:
a list of dictionaries, each entry corresponding to the qng for 1 argument of objective, in the order
of said objectives.
"""
combos = []
vars = objective.extract_variables()
compiled = compile_multitarget(gate=objective)
compiled = compile_trotterized_gate(gate=compiled)
compiled = compile_h_power(gate=compiled)
compiled = compile_power_gate(gate=compiled)
compiled = compile_controlled_phase(gate=compiled)
compiled = compile_controlled_rotation(gate=compiled)
for i, arg in enumerate(compiled.args):
if not isinstance(arg, ExpectationValueImpl):
### this is a variable, no QNG involved
mat = QngMatrix([[[1]]])
vec = CallableVector([__grad_inner(arg, arg)])
mapping = {0: {v: __grad_inner(arg, v) for v in vars}}
else:
### if the arg is an expectationvalue, we need to build some qngs and mappings!
blocks = func(arg,
initial_values=initial_values,
samples=samples,
device=device,
backend=backend,
noise=noise)
mat = QngMatrix(blocks)
vec = subvector_procedure(arg,
initial_values=initial_values,
samples=samples,
device=device,
backend=backend,
noise=noise)
mapping = {}
self_pars = get_self_pars(arg.U)
for j, p in enumerate(self_pars):
indict = {}
for v in p.extract_variables():
gi = __grad_inner(p, v)
if isinstance(gi, Objective):
g = compile_objective(gi,
variables=initial_values,
samples=samples,
device=device,
backend=backend,
noise=noise)
else:
g = gi
indict[v] = g
mapping[j] = indict
posarg = jax.grad(compiled.transformation, i)
p = Objective(compiled.args, transformation=posarg)
pos = compile_objective(p,
variables=initial_values,
samples=samples,
device=device,
backend=backend,
noise=noise)
combos.append(qng_dict(arg, mat, vec, mapping, pos))
return combos
def get_qng_combos(objective,
initial_values=None,
samples=None,
backend=None,
backend_options=None,
noise=None):
combos = []
vars = objective.extract_variables()
compiled = compile_multitarget(gate=objective)
compiled = compile_trotterized_gate(gate=compiled)
compiled = compile_h_power(gate=compiled)
compiled = compile_power_gate(gate=compiled)
compiled = compile_controlled_phase(gate=compiled)
compiled = compile_controlled_rotation(gate=compiled)
for i, arg in enumerate(compiled.args):
if not isinstance(arg, ExpectationValueImpl):
### this is a variable, no QNG involved
mat = QngMatrix([[[1]]])
vec = CallableVector([__grad_inner(arg, arg)])
mapping = {0: {v: __grad_inner(arg, v) for v in vars}}
else:
### if the arg is an expectationvalue, we need to build some qngs and mappings!
blocks = qng_metric_tensor_blocks(arg,
initial_values=initial_values,
samples=samples,
backend=backend,
noise=noise,
backend_options=backend_options)
mat = QngMatrix(blocks)
vec = subvector_procedure(arg,
initial_values=initial_values,
samples=samples,
backend=backend,
noise=noise,
backend_options=backend_options)
mapping = {}
self_pars = get_self_pars(arg.U)
for j, p in enumerate(self_pars):
indict = {}
for v in p.extract_variables():
gi = __grad_inner(p, v)
if isinstance(gi, Objective):
g = compile_objective(gi,
variables=initial_values,
samples=samples,
backend=backend,
noise=noise,
backend_options=backend_options)
else:
g = gi
indict[v] = g
mapping[j] = indict
posarg = jax.grad(compiled.transformation, argnums=i)
p = Objective(compiled.args, transformation=posarg)
pos = compile_objective(p,
variables=initial_values,
samples=samples,
backend=backend,
noise=noise,
backend_options=backend_options)
combos.append(qng_dict(arg, mat, vec, mapping, pos))
return combos