Pennylane-forest

New features since last release

Improvements

* Updated the CI. [(63)](https://github.com/PennyLaneAI/pennylane-forest/pull/63)

Bug fixes

* Updated the plugin to be compatible with the new core of PennyLane. [(67)](https://github.com/PennyLaneAI/pennylane-forest/pull/67) [(#68)](https://github.com/PennyLaneAI/pennylane-forest/pull/68)

Documentation

* Adapted the documentation to the PennyLane theme. [(64)](https://github.com/PennyLaneAI/pennylane-forest/pull/64)

Contributors

This release contains contributions from (in alphabetical order):

Thomas Bromley, Theodor Isacsson, Josh Izaac, Maria Schuld, Antal Száva.

New features

* Forest devices now support custom wire labels. 55 57 58 59

One can now specify any string or number as a custom wire label, and use these labels to address subsystems on the device:

python
dev = qml.device('forest.qvm' device='4q-qvm', wires=['q1', 'ancilla', 0, 1])

qml.qnode(dev)
def circuit():
qml.Hadamard(wires='q1')
qml.CNOT(wires=[1, 'ancilla'])
return qml.expval(qml.PauliZ(0))


Bug fixes

* Fixes a bug where basis states were not correctly initialized 56

* Tensor measurements have been fixed due to a change in PL v0.11 52

* The QCS no longer supports lattice-based devices such as Aspen-4-5Q-E. As a result, the `forest.qvm` and `forest.qpu` devices no longer accept lattice-based devices, and must be loaded with the full device name (e.g. Aspen-4). 51

Contributors

Theodor Isacsson, Josh Izaac, Maria Schuld, Antal Száva

* Adding support for multi-qubit observable estimation.

* Added a new method `analytic_probability()` to the `Wavefunction` simulator devices, so that analytic and non-analytic probabilities are correctly returned.

* Reduced the default number of shots for the `QPUDevice` to 1000 from 1024.

* The test suite now programmatically queries `pyquil.list_quantum_computers()` for valid QPU lattices for integration tests, and additional gradient integration tests have been added.

* The minimum required version of PennyLane has been increased to v0.9 due to plugin API changes in PennyLane.

* Added parametric compilation for `forest.qvm` and `forest.qpu`, leading to significant speed increases when performing optimization

* The plugin has been ported to use the new [`QubitDevice`](https://pennylane.readthedocs.io/en/stable/code/api/pennylane.QubitDevice.html) API for plugins, resulting in a cleaner interface with built-in support for multi-qubit observables.

* Bug fixes to allow the plugin to work with pyQuil >= 2.16

* Operator estimation on `forest.qpu` now works with both 1 and 2 qubit gates

* Adds a converter so that pyQuil and Quil programs can be loaded as differentiable PennyLane templates

* Added ability to return multi-qubit expectation values to all devices

* Added variance support

* Replaced dense matrix expansion with a tensordot implementation

* Added support for `samples`

* Readout Error Mitigation using Operator Estimation

* Updates the plugin to work with PyQuil 2.13 and PennyLane 0.6