def __call__(self, *args, **kwargs):
if self.interface == "autograd":
# HOTFIX: to maintain compatibility with core, here we treat
# all inputs that do not explicitly specify `requires_grad=False`
# as trainable. This should be removed at some point, forcing users
# to specify `requires_grad=True` for trainable parameters.
args = [
anp.array(a, requires_grad=True)
if not hasattr(a, "requires_grad") else a for a in args
]
# construct the tape
self.construct(args, kwargs)
# execute the tape
res = self.qtape.execute(device=self.device)
if isinstance(self.qfunc_output, Sequence):
return res
# HOTFIX: Output is a single measurement function. To maintain compatibility
# with core, we squeeze all outputs.
# Get the namespace associated with the return type
res_type_namespace = res.__class__.__module__.split(".")[0]
if res_type_namespace in ("pennylane", "autograd"):
# For PennyLane and autograd we must branch, since
# 'squeeze' does not exist in the top-level of the namespace
return anp.squeeze(res)
# Same for JAX
if res_type_namespace == "jax":
return __import__(res_type_namespace).numpy.squeeze(res)
return __import__(res_type_namespace).squeeze(res)
def __call__(self, *args, **kwargs):
# construct the tape
self.construct(args, kwargs)
# execute the tape
res = self.qtape.execute(device=self.device)
# FIX: If the qnode swapped the device, increase the num_execution value on the original device.
# In the long run, we should make sure that the user's device is the one
# actually run so she has full control. This could be done by changing the class
# of the user's device before and after executing the tape.
if self.device is not self._original_device:
self._original_device._num_executions += 1 # pylint: disable=protected-access
if isinstance(self.qfunc_output, Sequence):
return res
# HOTFIX: Output is a single measurement function. To maintain compatibility
# with core, we squeeze all outputs.
# Get the namespace associated with the return type
res_type_namespace = res.__class__.__module__.split(".")[0]
if res_type_namespace in ("pennylane", "autograd"):
# For PennyLane and autograd we must branch, since
# 'squeeze' does not exist in the top-level of the namespace
return anp.squeeze(res)
# Same for JAX
if res_type_namespace == "jax":
return __import__(res_type_namespace).numpy.squeeze(res)
return __import__(res_type_namespace).squeeze(res)
def __call__(self, *args, **kwargs):
if self.interface == "autograd":
# HOTFIX: to maintain compatibility with core, here we treat
# all inputs that do not explicitly specify `requires_grad=False`
# as trainable. This should be removed at some point, forcing users
# to specify `requires_grad=True` for trainable parameters.
args = [
anp.array(a, requires_grad=True)
if not hasattr(a, "requires_grad") else a for a in args
]
# construct the tape
self.construct(args, kwargs)
if self._caching:
# Every time the QNode is called, it creates a new tape. We want the tape cache to
# persist over multiple tapes, so hence keep track of it as a QNode attribute and
# load it into the new tape
self.qtape._cache_execute = self._cache_execute
# execute the tape
res = self.qtape.execute(device=self.device)
# HOTFIX: to maintain compatibility with core, we squeeze
# all outputs.
# Get the namespace associated with the return type
res_type_namespace = res.__class__.__module__.split(".")[0]
if res_type_namespace in ("pennylane", "autograd"):
# For PennyLane and autograd we must branch, since
# 'squeeze' does not exist in the top-level of the namespace
return anp.squeeze(res)
if self._caching:
self._cache_execute = self.qtape._cache_execute
return __import__(res_type_namespace).squeeze(res)