New features since last release
* Application of debiasing and sharpening for error mitigation is made available, with parameters set on device initialization. Error mitigation strategies that
need to be set at runtime are defined in the `error_mitigation` dictionary (currently a single strategy, `debias`, is available). Whether or not to
apply sharpening to the returned results is set via the parameter `sharpen`. A device using debiasing and sharpening to mitigate errors can be initialized as:
python
import pennylane as qml
dev = qml.device("ionq.qpu", wires=2, error_mitigation={"debias": True}, sharpen=True)
For more details, see the [IonQ Guide on sharpening and debiasing](https://ionq.com/resources/debiasing-and-sharpening), or refer to the publication <https://arxiv.org/pdf/2301.07233.pdf>
[(75)](https://github.com/PennyLaneAI/PennyLane-IonQ/pull/75)
[(96)](https://github.com/PennyLaneAI/PennyLane-IonQ/pull/96)
Improvements 🛠
* The IonQ API version accessed via the plugin is updated from 0.1 to 0.3
[(75)](https://github.com/PennyLaneAI/PennyLane-IonQ/pull/75)
[(96)](https://github.com/PennyLaneAI/PennyLane-IonQ/pull/96)
* Use new `backend` field to specify `qpu`.
[(81)](https://github.com/PennyLaneAI/PennyLane-IonQ/pull/81)
Contributors ✍️
This release contains contributions from (in alphabetical order):
Spencer Churchill
Lillian Frederiksen
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