Source code for pennylane

# Copyright 2018 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.
"""
.. _library_overview:

Overview
========

The PennyLane codebase contains a number of complementary components.
These can be roughly separated into a user-interface, supported core
operations, and a developer API.

Software components
-------------------

The main user-interface to PennyLane. These are the functions and
classes that will be used by a majority of users. For a good introduction
on the user-interface of PennyLane, have a look at our tutorials.

**Basic functionality**

* The device loader: :func:`pennylane.device`
* The quantum node object: :mod:`pennylane.QNode <pennylane.qnode>`
* The QNode decorator: :mod:`pennylane.qnode <pennylane.decorator>`

**Extra modules**

* Optimization methods: :mod:`pennylane.optimize`
* Configuration: :mod:`pennylane.Configuration <pennylane.configuration>`
* NumPy with support for automatic differentiation: :mod:`pennylane.numpy <pennylane.numpy>`

**Core operations**

The main operations and observables supported by PennyLane.
Each of the operations supports a method
of computing gradients (either analytically or numerically).

The conventions used in defining these operations are also
provided here.

* Supported operations and observables: :mod:`pennylane.ops`
* Supported measurement types: :mod:`pennylane.measure`

**Developer API**

Used to develop new plugins for PennyLane - providing new devices
for QNodes, or supporting new operations and expectations. For more
details, see :ref:`developer_overview`.

* The base Device class: :mod:`pennylane.Device <pennylane._device>`
* Utilities: :mod:`pennylane.utils <pennylane.utils>`
* Symbolic quantum operations: :mod:`pennylane.operation`
* Quantum circuit parameters: :mod:`pennylane.variable`

Summary
-------

.. autosummary::


    ~configuration.Configuration
    ~_device.Device
    ~_device.DeviceError
    device
    grad
    jacobian
    ~autograd.numpy
    ops
    optimize
    ~qnode.QNode
    ~decorator.qnode
    ~qnode.QuantumFunctionError
    version

.. note::

    All individual operations and observables (contained in :mod:`~.ops`),
    measurements (contained in :mod:`~.measure`), and optimizers
    (contained in :mod:`~.optimize`) may also be imported directly from PennyLane.

Code details
~~~~~~~~~~~~
"""
import os
from pkg_resources import iter_entry_points

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

from semantic_version import Version, Spec

import pennylane.operation

import pennylane.init
import pennylane.templates.layers
import pennylane.templates.embeddings
from pennylane.about import about


from .configuration import Configuration
from ._device import Device, DeviceError
from .measure import expval, var, sample
from .ops import *
from .optimize import *
from .qnode import QNode, QuantumFunctionError
from ._version import __version__

# NOTE: this has to be imported last,
# otherwise it will clash with the .qnode import.
from .decorator import qnode


# overwrite module docstrings
numpy.__doc__ = "NumPy with automatic differentiation support, provided by Autograd."


# 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 iter_entry_points("pennylane.plugins")}


[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 two devices: * :mod:`'default.qubit' <pennylane.plugins.default_qubit>`: a simple pure state simulator of qubit-based quantum circuit architectures. * :mod:`'default.gaussian' <pennylane.plugins.default_gaussian>`: a simple simulator of Gaussian states and operations on continuous-variable circuit architectures. In addition, additional devices are supported through plugins — see :ref:`plugins` for more details. All devices must be loaded by specifying their **short-name** as listed above, followed by the number of *wires* (subsystems) you wish to initialize. 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): an 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): """Returns the gradient (as a callable function) of functions accessible within PennyLane. This is a wrapper around the :mod:`autograd.grad` function. Args: func (function): a Python function or QNode that contains a combination of quantum and classical nodes argnum (int or list(int)): which argument(s) to take the gradient with respect to Returns: function: the function that returns the gradient of the input function with respect to the arguments in argnum """ # pylint: disable=no-value-for-parameter return _grad(func, argnum)
[docs]def jacobian(func, argnum): """Returns the Jacobian (as a callable function) of vector-valued functions accessible within PennyLane. 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 isinstance(argnum, int): return _jacobian(func, argnum) return lambda *args, **kwargs: numpy.stack([_jacobian(func, arg)(*args, **kwargs) for arg in argnum]).T
[docs]def version(): """Returns the PennyLane version number.""" return __version__