PennyLaneAI/pennylane v0.10.0 on GitHub

New features since last release

New and improved simulators

Added a new device, default.qubit.tf, a pure-state qubit simulator written using TensorFlow. As a result, it supports classical backpropagation as a means to compute the Jacobian. This can be faster than the parameter-shift rule for computing quantum gradients when the number of parameters to be optimized is large.
default.qubit.tf is designed to be used with end-to-end classical backpropagation (diff_method="backprop") with the TensorFlow interface. This is the default method of differentiation when creating a QNode with this device.
Using this method, the created QNode is a 'white-box' that is tightly integrated with your TensorFlow computation, including AutoGraph support:
```
>>> dev = qml.device("default.qubit.tf", wires=1)
>>> @tf.function
... @qml.qnode(dev, interface="tf", diff_method="backprop")
... def circuit(x):
...     qml.RX(x[1], wires=0)
...     qml.Rot(x[0], x[1], x[2], wires=0)
...     return qml.expval(qml.PauliZ(0))
>>> weights = tf.Variable([0.2, 0.5, 0.1])
>>> with tf.GradientTape() as tape:
...     res = circuit(weights)
>>> print(tape.gradient(res, weights))
tf.Tensor([-2.2526717e-01 -1.0086454e+00  1.3877788e-17], shape=(3,), dtype=float32)
```
See the default.qubit.tf documentation for more details.
The default.tensor plugin has been significantly upgraded. It now allows two different tensor network representations to be used: "exact" and "mps". The former uses a exact factorized representation of quantum states, while the latter uses a matrix product state representation. (#572) (#599)

New machine learning functionality and integrations

PennyLane QNodes can now be converted into Torch layers, allowing for creation of quantum and hybrid models using the torch.nn API. (#588)

A PennyLane QNode can be converted into a torch.nn layer using the qml.qnn.TorchLayer class:

>>> @qml.qnode(dev)
... def qnode(inputs, weights_0, weight_1):
...    # define the circuit
...    # ...

>>> weight_shapes = {"weights_0": 3, "weight_1": 1}
>>> qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)

A hybrid model can then be easily constructed:

>>> model = torch.nn.Sequential(qlayer, torch.nn.Linear(2, 2))

Added a new "reversible" differentiation method which can be used in simulators, but not hardware.
The reversible approach is similar to backpropagation, but trades off extra computation for enhanced memory efficiency. Where backpropagation caches the state tensors at each step during a simulated evolution, the reversible method only caches the final pre-measurement state.
Compared to the parameter-shift method, the reversible method can be faster or slower, depending on the density and location of parametrized gates in a circuit (circuits with higher density of parametrized gates near the end of the circuit will see a benefit). (#670)
```
>>> dev = qml.device("default.qubit", wires=2)
... @qml.qnode(dev, diff_method="reversible")
... def circuit(x):
...     qml.RX(x, wires=0)
...     qml.RX(x, wires=0)
...     qml.CNOT(wires=[0,1])
...     return qml.expval(qml.PauliZ(0))
>>> qml.grad(circuit)(0.5)
(array(-0.47942554),)
```

New templates and cost functions

Added the new templates UCCSD, SingleExcitationUnitary, andDoubleExcitationUnitary, which together implement the Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz to perform VQE-based quantum chemistry simulations using PennyLane-QChem. (#622) (#638) (#654) (#659) (#622)
Added module pennylane.qnn.cost with class SquaredErrorLoss. The module contains classes to calculate losses and cost functions on circuits with trainable parameters. (#642)

Improvements

A significant improvement with respect to how QNodes and interfaces mark quantum function arguments as differentiable when using Autograd, designed to improve performance and make QNodes more intuitive. (#648) (#650)
In particular, the following changes have been made:
- A new ndarray subclass pennylane.numpy.tensor, which extends NumPy arrays with the keyword argument and attribute requires_grad. Tensors which have requires_grad=False are treated as non-differentiable by the Autograd interface.
- A new subpackage pennylane.numpy, which wraps autograd.numpy such that NumPy functions accept the requires_grad keyword argument, and allows Autograd to differentiate pennylane.numpy.tensor objects.
- The argnum argument to qml.grad is now optional; if not provided, arguments explicitly marked as requires_grad=False are excluded for the list of differentiable arguments. The ability to pass argnum has been retained for backwards compatibility, and if present the old behaviour persists.
The QNode Torch interface now inspects QNode positional arguments. If any argument does not have the attribute requires_grad=True, it is automatically excluded from quantum gradient computations. (#652) (#660)
The QNode TF interface now inspects QNode positional arguments. If any argument is not being watched by a tf.GradientTape(), it is automatically excluded from quantum gradient computations. (#655) (#660)
QNodes have two new public methods: QNode.set_trainable_args() and QNode.get_trainable_args(). These are designed to be called by interfaces, to specify to the QNode which of its input arguments are differentiable. Arguments which are non-differentiable will not be converted to PennyLane Variable objects within the QNode. (#660)
Added decomposition method to PauliX, PauliY, PauliZ, S, T, Hadamard, and PhaseShift gates, which decomposes each of these gates into rotation gates. (#668)
The CircuitGraph class now supports serializing contained circuit operations and measurement basis rotations to an OpenQASM2.0 script via the new CircuitGraph.to_openqasm() method. (#623)

Breaking changes

Removes support for Python 3.5. (#639)

Documentation

Various small typos were fixed.

Contributors

This release contains contributions from (in alphabetical order):

Thomas Bromley, Jack Ceroni, Alain Delgado Gran, Theodor Isacsson, Josh Izaac, Nathan Killoran, Maria Schuld, Antal Száva, Nicola Vitucci.

PennyLaneAI/pennylane v0.10.0 Release 0.10.0 on GitHub