def _build_plan(self, plan_factory, device_params, alpha, beta, seed):
plan = plan_factory()
bijection = philox(64, 2)
# Keeping the kernel the same so it can be cached.
# The seed will be passed as the computation parameter instead.
keygen = KeyGenerator.create(bijection, seed=numpy.int32(0))
sampler = normal_bm(bijection, numpy.float64)
squeezing = plan.persistent_array(self._system.squeezing)
decoherence = plan.persistent_array(self._system.decoherence)
plan.kernel_call(TEMPLATE.get_def("generate_input_state"),
[alpha, beta, squeezing, decoherence, seed],
kernel_name="generate",
global_size=alpha.shape,
render_kwds=dict(
system=self._system,
representation=self._representation,
Representation=Representation,
bijection=bijection,
keygen=keygen,
sampler=sampler,
ordering=ordering,
exp=functions.exp(numpy.float64),
mul_cr=functions.mul(numpy.complex128,
numpy.float64),
add_cc=functions.add(numpy.complex128,
numpy.complex128),
))
return plan
def test_computation_general(thr_and_double):
size = 10000
batch = 101
thr, double = thr_and_double
dtype = numpy.float64 if double else numpy.float32
mean, std = -2, 10
bijection = philox(64, 4)
sampler = normal_bm(bijection, dtype, mean=mean, std=std)
rng = CBRNG(Type(dtype, shape=(batch, size)), 1, sampler)
check_computation(thr, rng, mean=mean, std=std)
def test_computation_general(thr_and_double):
size = 10000
batch = 101
thr, double = thr_and_double
dtype = numpy.float64 if double else numpy.float32
mean, std = -2, 10
bijection = philox(64, 4)
sampler = normal_bm(bijection, dtype, mean=mean, std=std)
rng = CBRNG(Type(dtype, shape=(batch, size)), 1, sampler)
check_computation(thr, rng, mean=mean, std=std)
def get_sampler(self, double):
dtype = numpy.complex128 if double else numpy.complex64
return normal_bm(self.bijection, dtype, mean=self.mean, std=self.std)
def get_sampler(self, double):
dtype = numpy.float64 if double else numpy.float32
return normal_bm(self.bijection, dtype, mean=self.mean, std=self.std)
def _build_plan(self, plan_factory, device_params, alpha, beta, alpha_i,
beta_i, seed):
plan = plan_factory()
system = self._system
representation = self._representation
unitary = plan.persistent_array(self._system.unitary)
needs_noise_matrix = representation != Representation.POSITIVE_P and system.needs_noise_matrix(
)
mmul = MatrixMul(alpha, unitary, transposed_b=True)
if not needs_noise_matrix:
# TODO: this could be sped up for repr != POSITIVE_P,
# since in that case alpha == conj(beta), and we don't need to do two multuplications.
mmul_beta = MatrixMul(beta, unitary, transposed_b=True)
trf_conj = self._make_trf_conj()
mmul_beta.parameter.matrix_b.connect(trf_conj,
trf_conj.output,
matrix_b_p=trf_conj.input)
plan.computation_call(mmul, alpha, alpha_i, unitary)
plan.computation_call(mmul_beta, beta, beta_i, unitary)
else:
noise_matrix = system.noise_matrix()
noise_matrix_dev = plan.persistent_array(noise_matrix)
# If we're here, it's not positive-P, and alpha == conj(beta).
# This means we can just calculate alpha, and then build beta from it.
w = plan.temp_array_like(alpha)
temp_alpha = plan.temp_array_like(alpha)
plan.computation_call(mmul, temp_alpha, alpha_i, unitary)
bijection = philox(64, 2)
# Keeping the kernel the same so it can be cached.
# The seed will be passed as the computation parameter instead.
keygen = KeyGenerator.create(bijection, seed=numpy.int32(0))
sampler = normal_bm(bijection, numpy.float64)
plan.kernel_call(TEMPLATE.get_def("generate_apply_matrix_noise"),
[w, seed],
kernel_name="generate_apply_matrix_noise",
global_size=alpha.shape,
render_kwds=dict(
bijection=bijection,
keygen=keygen,
sampler=sampler,
mul_cr=functions.mul(numpy.complex128,
numpy.float64),
add_cc=functions.add(numpy.complex128,
numpy.complex128),
))
noise = plan.temp_array_like(alpha)
plan.computation_call(mmul, noise, w, noise_matrix_dev)
plan.kernel_call(TEMPLATE.get_def("add_noise"),
[alpha, beta, temp_alpha, noise],
kernel_name="add_noise",
global_size=alpha.shape,
render_kwds=dict(
add=functions.add(numpy.complex128,
numpy.complex128),
conj=functions.conj(numpy.complex128)))
return plan
def get_sampler(self, bijection, double):
dtype = numpy.complex128 if double else numpy.complex64
return normal_bm(bijection, dtype, mean=self.mean, std=self.std)
def get_sampler(self, bijection, double):
dtype = numpy.float64 if double else numpy.float32
return normal_bm(bijection, dtype, mean=self.mean, std=self.std)