Source code for pennylane.capture.capture_qnode
# Copyright 2024 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This submodule defines a capture compatible call to QNodes.
"""
import warnings
from copy import copy
from dataclasses import asdict
from functools import lru_cache, partial
import pennylane as qml
from .flatfn import FlatFn
has_jax = True
try:
import jax
except ImportError:
has_jax = False
def _get_shapes_for(*measurements, shots=None, num_device_wires=0):
if jax.config.jax_enable_x64: # pylint: disable=no-member
dtype_map = {
float: jax.numpy.float64,
int: jax.numpy.int64,
complex: jax.numpy.complex128,
}
else:
dtype_map = {
float: jax.numpy.float32,
int: jax.numpy.int32,
complex: jax.numpy.complex64,
}
shapes = []
if not shots:
shots = [None]
for s in shots:
for m in measurements:
shape, dtype = m.aval.abstract_eval(shots=s, num_device_wires=num_device_wires)
shapes.append(jax.core.ShapedArray(shape, dtype_map.get(dtype, dtype)))
return shapes
@lru_cache()
def _get_qnode_prim():
if not has_jax:
return None
qnode_prim = jax.core.Primitive("qnode")
qnode_prim.multiple_results = True
# pylint: disable=too-many-arguments
@qnode_prim.def_impl
def _(*args, qnode, shots, device, qnode_kwargs, qfunc_jaxpr, n_consts):
consts = args[:n_consts]
args = args[n_consts:]
def qfunc(*inner_args):
return jax.core.eval_jaxpr(qfunc_jaxpr, consts, *inner_args)
with warnings.catch_warnings():
warnings.filterwarnings(
action="ignore",
message=r"The max_expansion argument is deprecated and will be removed in version 0.39.",
category=qml.PennyLaneDeprecationWarning,
)
qnode = qml.QNode(qfunc, device, **qnode_kwargs)
return qnode._impl_call(*args, shots=shots) # pylint: disable=protected-access
# pylint: disable=unused-argument
@qnode_prim.def_abstract_eval
def _(*args, qnode, shots, device, qnode_kwargs, qfunc_jaxpr, n_consts):
mps = qfunc_jaxpr.outvars
return _get_shapes_for(*mps, shots=shots, num_device_wires=len(device.wires))
return qnode_prim
[docs]def qnode_call(qnode: "qml.QNode", *args, **kwargs) -> "qml.typing.Result":
"""A capture compatible call to a QNode. This function is internally used by ``QNode.__call__``.
Args:
qnode (QNode): a QNode
args: the arguments the QNode is called with
Keyword Args:
kwargs (Any): Any keyword arguments accepted by the quantum function
Returns:
qml.typing.Result: the result of a qnode execution
**Example:**
.. code-block:: python
qml.capture.enable()
@qml.qnode(qml.device('lightning.qubit', wires=1))
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.Z(0)), qml.probs()
def f(x):
expval_z, probs = circuit(np.pi * x, shots=50000)
return 2 * expval_z + probs
jaxpr = jax.make_jaxpr(f)(0.1)
print("jaxpr:")
print(jaxpr)
res = jax.core.eval_jaxpr(jaxpr.jaxpr, jaxpr.consts, 0.7)
print()
print("result:")
print(res)
.. code-block:: none
jaxpr:
{ lambda ; a:f32[]. let
b:f32[] = mul 3.141592653589793 a
c:f32[] d:f32[2] = qnode[
device=<lightning.qubit device (wires=1) at 0x10557a070>
qfunc_jaxpr={ lambda ; e:f32[]. let
_:AbstractOperator() = RX[n_wires=1] e 0
f:AbstractOperator() = PauliZ[n_wires=1] 0
g:AbstractMeasurement(n_wires=None) = expval_obs f
h:AbstractMeasurement(n_wires=0) = probs_wires
in (g, h) }
qnode=<QNode: device='<lightning.qubit device (wires=1) at 0x10557a070>', interface='auto', diff_method='best'>
qnode_kwargs={'diff_method': 'best', 'grad_on_execution': 'best', 'cache': False, 'cachesize': 10000, 'max_diff': 1, 'max_expansion': 10, 'device_vjp': False, 'mcm_method': None, 'postselect_mode': None}
shots=Shots(total=50000)
] b
i:f32[] = mul 2.0 c
j:f32[2] = add i d
in (j,) }
result:
[Array([-0.96939224, -0.38207346], dtype=float32)]
"""
if "shots" in kwargs:
shots = qml.measurements.Shots(kwargs.pop("shots"))
else:
shots = qnode.device.shots
if shots.has_partitioned_shots:
# Questions over the pytrees and the nested result object shape
raise NotImplementedError("shot vectors are not yet supported with plxpr capture.")
if not qnode.device.wires:
raise NotImplementedError("devices must specify wires for integration with plxpr capture.")
qfunc = partial(qnode.func, **kwargs) if kwargs else qnode.func
flat_fn = FlatFn(qfunc)
qfunc_jaxpr = jax.make_jaxpr(flat_fn)(*args)
execute_kwargs = copy(qnode.execute_kwargs)
mcm_config = asdict(execute_kwargs.pop("mcm_config"))
qnode_kwargs = {"diff_method": qnode.diff_method, **execute_kwargs, **mcm_config}
qnode_prim = _get_qnode_prim()
flat_args, _ = jax.tree_util.tree_flatten(args)
res = qnode_prim.bind(
*qfunc_jaxpr.consts,
*flat_args,
shots=shots,
qnode=qnode,
device=qnode.device,
qnode_kwargs=qnode_kwargs,
qfunc_jaxpr=qfunc_jaxpr.jaxpr,
n_consts=len(qfunc_jaxpr.consts),
)
assert flat_fn.out_tree is not None, "out_tree should be set by call to flat_fn"
return jax.tree_util.tree_unflatten(flat_fn.out_tree, res)
_modules/pennylane/capture/capture_qnode
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