Source code for pennylane.collections.qnode_collection

# Copyright 2018-2020 Xanadu Quantum Technologies Inc.

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Contains the QNodeCollection class.
# pylint: disable=too-many-arguments,import-outside-toplevel
from import Sequence
import warnings

[docs]class QNodeCollection(Sequence): """Represents a sequence of independent QNodes that all share the same signature. When the collection is evaluated, all QNodes are simultaneously evaluated with the same parameters. All QNodes within a QNodeCollection **must** use the same interface. .. note:: the recommended method of creating a QNodeCollection is via :func:``. Args: qnodes (None or List[QNode]): A list of QNodes sharing the same signature. If not provided, an empty QNode collection is instantiated. .. seealso:: :func:``, :func:`~.apply`, :func:`~.sum`, :func:`` **Example:** A QNodeCollection can be created using a list of existing QNodes: >>> qnode = qml.QNodeCollection([qnode1, qnode2]) Instantiating a QNode collection with no arguments creates an empty collection: >>> qnodes = qml.QNodeCollection() >>> len(qnodes) 0 QNodes can be appended: >>> qnodes.append(qnode1) >>> len(qnodes) 1 or extended: >>> qnodes.extend([qnode2, qnode3]) >>> len(qnodes) 3 They can also be indexed: >>> qnodes[0] <QNode: device='default.qubit', func=circuit, wires=2, interface=torch> or looped over: >>> [i.num_wires for i in qnodes] [2, 2, 2] To evaluate a QNodeCollection, simply call the collection, passing the parameters as required by the constituent QNode. For example, consider the following two QNodes with the same signature: .. code-block:: python3 dev1 = qml.device("default.qubit", wires=1) dev2 = qml.device("default.qubit", wires=2) @qml.qnode(dev1) def qnode1(x, y): qml.RX(x, wires=0) qml.RY(y, wires=0) return qml.expval(qml.PauliZ(0)) @qml.qnode(dev2) def qnode2(x, y): qml.Hadamard(wires=0) qml.RX(x, wires=0) qml.RY(y, wires=1) qml.CNOT(wires=[0, 1]) return qml.expval(qml.PauliZ(0)), qml.var(qml.PauliZ(1)) Creating a QNodeCollection, >>> qnodes = qml.QNodeCollection([qnode1, qnode2]) We can evaluate this QNode collection directly: >>> qnodes(0.5643, -0.45) [ 7.60844651e-01 -5.55111512e-17 1.00000000e+00] where the results from each QNode have been flattened and concatenated into a single one-dimensional list. .. raw:: html <h2>Asynchronous evaluation</h2> .. warning:: You will find the best speedups when using asynchronous mode when QNodes are to be evaluated on external hardware devices or external simulators. **It is not advised at this point to use asynchronous mode with** ``default.qubit`` **.** .. warning:: Asynchronous evaluation is experimental --- please report all bugs and issues to our GitHub page. It currently works with all interfaces, however backpropagation and gradient computation is limited to Autograd and PyTorch. **Quantum gradients using TensorFlow in asynchronous mode is currently not supported**. By default, the QNodes within the QNodeCollection are executed sequentially. However, experimental asynchronous support is now available using the `Dask <>`_ parallelism library. This can be activated by passing the ``parallel=True`` keyword argument when evaluating the QNodeCollection. For example, let's create the following two QVM simulation devices: >>> qpu1 = qml.device("forest.qvm", device="Aspen-4-4Q-D") >>> qpu2 = qml.device("forest.qvm", device="Aspen-7-4Q-B") We can create a collection of QNodes with different observables by mapping an ansatz over these devices using :func:``: >>> obs_list = [qml.PauliX(0), qml.PauliZ(0) @ qml.PauliZ(1)] >>> qnodes =, obs_list, [qpu1, qpu2]) We can now create some parameters and evaluate the collection: >>> params = qml.init.strong_ent_layers_normal(n_layers=4, n_wires=4) >>> qnodes(params) array([0.046875 , 0.93164062]) The above collection was executed sequentially. Executing it in parallel: >>> qnodes(params, parallel=True) array([0.0234375 , 0.92578125]) We can time both approaches from within IPython or a Jupyter notebook: >>> %timeit qnodes(params) 5.16 s ± 162 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) >>> %timeit qnodes(params, parallel=True) 2.99 s ± 40.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) """ def __init__(self, qnodes=None): self.qnodes = [] self.extend(qnodes or []) @property def interface(self): """str, None: automatic differentiation interface used by the collection, if any""" if not self.qnodes: return None return self.qnodes[0].interface
[docs] def append(self, qnode): """Appends a QNode to the collection. The appended QNode *must* have the same interface as the QNode collection.""" self.extend([qnode])
[docs] def extend(self, qnodes): """Extends the collection by a list of QNodes. The appended QNodes *must* have the same interface as the QNode collection.""" if not all(i.interface == qnodes[0].interface for i in qnodes): raise ValueError("Provided QNodes do not all use the same interface") if self.qnodes and (qnodes[0].interface != self.interface): raise ValueError( "Interface mismatch. Provided QNodes use the {} interface, " "QNode collection uses the {} interface".format(qnodes[0].interface, self.interface) ) self.qnodes.extend(qnodes)
[docs] def evaluate(self, args, kwargs): """Evaluate all QNodes in the collection. Args: args (list): list containing the arguments to pass to all internal QNodes kwargs (dict): dictionary containing the keyword arguments to pass to all internal QNodes Returns: list: the results from each QNode """ results = [] parallel = kwargs.pop("parallel", False) _scheduler = kwargs.pop("scheduler", "threads") if parallel: try: import dask except: raise ImportError( "Dask must be installed for parallel evaluation. " "\nDask can be installed using pip:" "\n\npip install dask[delayed]" ) if self.interface == "tf": warnings.warn( "Parallel execution of QNodeCollections is " "an experimental feature, and currently doesn't " "work with TensorFlow backpropagation. Please use " "the PyTorch or Autograd interfaces instead.", UserWarning, ) for q in self.qnodes: results.append(dask.delayed(q)(*args, **kwargs)) return dask.compute(*results, scheduler=_scheduler) for q in self.qnodes: results.append(q(*args, **kwargs)) return results
[docs] @staticmethod def convert_results(results, interface): """Convert a list of results coming from multiple QNodes to the object required by each interface for auto-differentiation. Internally, this method makes use of ``tf.stack``, ``torch.stack``, and ``np.vstack``. Args: results (list): list containing the results from multiple QNodes interface (str): the interfaces of the underlying QNodes Returns: list or array or torch.Tensor or tf.Tensor: the converted and stacked results. """ if interface == "tf": import tensorflow as tf return tf.stack(results) if interface == "torch": import torch return torch.stack(results, dim=0) if interface in ("autograd", "numpy"): from autograd import numpy as np return np.stack(results) return results
[docs] def __call__(self, *args, **kwargs): results = self.evaluate(args, kwargs) return self.convert_results(results, self.interface)
def __len__(self): return len(self.qnodes) def __getitem__(self, idx): return self.qnodes[idx]