Source code for pennylane

# Copyright 2018-2020 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 is the top level module from which all basic functions and classes of
PennyLane can be directly imported.
"""
import pkg_resources

import numpy as _np
from autograd import grad as _grad
from autograd import jacobian as _jacobian

from semantic_version import Version, Spec

# QueuingContext needs to be imported before all other pennylane imports
from ._queuing import QueuingContext  # pylint: disable=wrong-import-order
import pennylane.operation

import pennylane.init
import pennylane.templates
import pennylane.qnn
import pennylane.qaoa as qaoa
from pennylane.templates import template, broadcast, layer
from pennylane.about import about
from pennylane.vqe import Hamiltonian, VQECost

from .circuit_graph import CircuitGraph
from .configuration import Configuration
from ._device import Device, DeviceError
from .collections import apply, map, sum, dot, QNodeCollection
from ._qubit_device import QubitDevice
from .measure import expval, var, sample, probs
from .ops import *
from .optimize import *
from .qnodes import qnode, QNode, QuantumFunctionError
from .utils import inv
from ._version import __version__
from .io import *

# Look for an existing configuration file
default_config = Configuration("config.toml")


# get list of installed plugin devices
plugin_devices = {
    entry.name: entry for entry in pkg_resources.iter_entry_points("pennylane.plugins")
}


# get chemistry plugin
[docs]class NestedAttrError: """This class mocks out the qchem module in case it is not installed. Any attempt to print an instance of this class, or to access an attribute of this class, results in an import error, directing the user to the installation instructions for PennyLane Qchem""" error_msg = ( "PennyLane-QChem not installed. \n\nTo access the qchem " "module, you can install PennyLane-QChem via pip:" "\n\npip install pennylane-qchem" "\n\nFor more details, see the quantum chemistry documentation:" "\nhttps://pennylane.readthedocs.io/en/stable/introduction/chemistry.html" ) def __str__(self): raise ImportError(self.error_msg) from None def __getattr__(self, name): raise ImportError(self.error_msg) from None __repr__ = __str__
qchem = NestedAttrError() for entry in pkg_resources.iter_entry_points("pennylane.qchem"): if entry.name == "OpenFermion": qchem = entry.load()
[docs]def device(name, *args, **kwargs): r"""device(name, wires=1, *args, **kwargs) Load a plugin :class:`~.Device` and return the instance. This function is used to load a particular quantum device, which can then be used to construct QNodes. PennyLane comes with support for the following devices: * :mod:`'default.qubit' <pennylane.devices.default_qubit>`: a simple state simulator of qubit-based quantum circuit architectures. * :mod:`'default.gaussian' <pennylane.devices.default_gaussian>`: a simple simulator of Gaussian states and operations on continuous-variable circuit architectures. * :mod:`'default.qubit.tf' <pennylane.devices.default_qubit.tf>`: a state simulator of qubit-based quantum circuit architectures written in TensorFlow, which allows automatic differentiation through the simulation. * :mod:`'default.qubit.autograd' <pennylane.devices.default_qubit.autograd>`: a state simulator of qubit-based quantum circuit architectures which allows automatic differentiation through the simulation via python's autograd library. In addition, additional devices are supported through plugins — see the `available plugins <https://pennylane.ai/plugins.html>`_ for more details. All devices must be loaded by specifying their **short-name** as listed above, followed by the **wires** (subsystems) you wish to initialize. The *wires* argument can be an integer, in which case the wires of the device are addressed by consecutive integers: .. code-block:: python dev = qml.device('default.qubit', wires=5) def circuit(): qml.Hadamard(wires=1) qml.Hadamard(wires=[0]) qml.CNOT(wires=[3, 4]) ... The *wires* argument can also be a sequence of unique numbers or strings, specifying custom wire labels that the user employs to address the wires: .. code-block:: python dev = qml.device('default.qubit', wires=['ancilla', 'q11', 'q12', -1, 1]) def circuit(): qml.Hadamard(wires='q11') qml.Hadamard(wires=['ancilla']) qml.CNOT(wires=['q12', -1] ) ... Some devices may accept additional arguments. For instance, ``default.gaussian`` accepts the keyword argument ``hbar``, to set the convention used in the commutation relation :math:`[\x,\p]=i\hbar` (by default set to 2). Please refer to the documentation for the individual devices to see any additional arguments that might be required or supported. Args: name (str): the name of the device to load wires (int): the number of wires (subsystems) to initialise the device with Keyword Args: config (pennylane.Configuration): a PennyLane configuration object that contains global and/or device specific configurations. """ if name in plugin_devices: options = {} # load global configuration settings if available config = kwargs.get("config", default_config) if config: # combine configuration options with keyword arguments. # Keyword arguments take preference, followed by device options, # followed by plugin options, followed by global options. options.update(config["main"]) options.update(config[name.split(".")[0] + ".global"]) options.update(config[name]) kwargs.pop("config", None) options.update(kwargs) # loads the plugin device class plugin_device_class = plugin_devices[name].load() if Version(version()) not in Spec(plugin_device_class.pennylane_requires): raise DeviceError( "The {} plugin requires PennyLane versions {}, however PennyLane " "version {} is installed.".format( name, plugin_device_class.pennylane_requires, __version__ ) ) # load plugin device return plugin_device_class(*args, **options) raise DeviceError("Device does not exist. Make sure the required plugin is installed.")
[docs]def grad(func, argnum=None): """Returns the gradient as a callable function of (functions of) QNodes. Function arguments with the property ``requires_grad`` set to ``False`` will automatically be excluded from the gradient computation, unless the ``argnum`` keyword argument is passed. Args: func (function): a plain QNode, or a Python function that contains a combination of quantum and classical nodes Keyword Args: argnum (int, list(int), None): Which argument(s) to take the gradient with respect to. By default, the arguments themselves are used to determine differentiability, by examining the ``requires_grad`` property. Providing this keyword argument overrides this behaviour, allowing argument differentiability to be defined manually for the returned gradient function. Returns: function: The function that returns the gradient of the input function with respect to the differentiable arguments, or, if specified, the arguments in ``argnum``. """ # pylint: disable=no-value-for-parameter if argnum is not None: # for backwards compatibility with existing code # that manually specifies argnum return _grad(func, argnum) def _gradient_function(*args, **kwargs): """Inspect the arguments for differentiability, and compute the autograd gradient function with required argnums dynamically. This wrapper function is returned to the user instead of autograd.grad, so that we can take into account cases where the user computes the gradient function once, but then calls it with arguments that change in differentiability. """ argnum = [] for idx, arg in enumerate(args): if getattr(arg, "requires_grad", True): argnum.append(idx) return _grad(func, argnum)(*args, **kwargs) return _gradient_function
[docs]def jacobian(func, argnum=None): """Returns the Jacobian as a callable function of vector-valued (functions of) QNodes. This is a wrapper around the :mod:`autograd.jacobian` function. Args: func (function): a vector-valued Python function or QNode that contains a combination of quantum and classical nodes. The output of the computation must consist of a single NumPy array (if classical) or a tuple of expectation values (if a quantum node) argnum (int or Sequence[int]): which argument to take the gradient with respect to. If a sequence is given, the Jacobian matrix corresponding to all input elements and all output elements is returned. Returns: function: the function that returns the Jacobian of the input function with respect to the arguments in argnum """ # pylint: disable=no-value-for-parameter if argnum is not None: # for backwards compatibility with existing code # that manually specifies argnum if isinstance(argnum, int): return _jacobian(func, argnum) return lambda *args, **kwargs: _np.stack( [_jacobian(func, arg)(*args, **kwargs) for arg in argnum] ).T def _jacobian_function(*args, **kwargs): """Inspect the arguments for differentiability, and compute the autograd gradient function with required argnums dynamically. This wrapper function is returned to the user instead of autograd.jacobian, so that we can take into account cases where the user computes the jacobian function once, but then calls it with arguments that change in differentiability. """ argnum = [] for idx, arg in enumerate(args): if getattr(arg, "requires_grad", True): argnum.append(idx) if len(argnum) == 1: return _jacobian(func, argnum[0])(*args, **kwargs) return _np.stack([_jacobian(func, arg)(*args, **kwargs) for arg in argnum]).T return _jacobian_function
[docs]def version(): """Returns the PennyLane version number.""" return __version__