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Contributor
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Hello. You may have forgotten to update the changelog!
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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Contributor
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Hi, do you have a preview for this? The typical docs preview https://xanaduai-pennylane--9370.com.readthedocs.build/en/9370/ does not appear to cover the Github too. |
Member
Author
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@drdren you can take a look here: https://github.com/PennyLaneAI/pennylane/blob/new-readme/README.md |
drdren
reviewed
Apr 28, 2026
| advanced features such as adaptive circuits, real-time measurement | ||
| feedback, and unbounded loops. See | ||
| [Catalyst](https://github.com/pennylaneai/catalyst) for more details. | ||
| PennyLane integrates with a wide range of [quantum hardware devices](https://pennylane.ai/devices). Whether superconducting qubits, trapped ion systems, neutral atoms, or photonics, PennyLane provides the tools to [estimate resources](https://pennylane.ai/qml/demos/re_how_to_use_pennylane_for_resource_estimation) and [compile circuits](https://staging.pennylane.ai/topics/quantum-compilation) specifically for the hardware devices of today—and tomorrow! |
Contributor
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You could consider linking to the demos we have about each of these hardware modalities?
drdren
requested changes
Apr 29, 2026
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| <a href="https://pennylane.ai">PennyLane</a> is an open-source quantum software platform | ||
| <a href="https://pennylane.ai/qml/quantum-computing/">quantum computing</a>, |
Contributor
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Perhaps this?
Suggested change
| <a href="https://pennylane.ai">PennyLane</a> is an open-source quantum software platform | |
| <a href="https://pennylane.ai/qml/quantum-computing/">quantum computing</a>, | |
| <a href="https://pennylane.ai">PennyLane</a> is an open-source quantum software platform for | |
| <a href="https://pennylane.ai/qml/quantum-computing/">quantum computing</a>, |
| - <strong>*Inspiration to implementation, quickly.*</strong> | ||
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| - <strong>*Machine learning with quantum hardware and simulators*</strong>. Integrate with **PyTorch**, **TensorFlow**, **JAX**, **Keras**, or **NumPy** to define and train hybrid models using quantum-aware optimizers and hardware-compatible gradients for advanced research tasks. [Quantum machine learning quickstart](https://docs.pennylane.ai/en/stable/introduction/interfaces.html). | ||
| Quantum computing can be complex — PennyLane makes it natural. Leverage the world’s largest library of [research demos](https://pennylane.ai/qml/demonstrations), [interactive tutorials](https://pennylane.ai/codebook/), and state-of-the-art components to build algorithms in [quantum chemistry](https://docs.pennylane.ai/en/stable/introduction/chemistry.html), quantum information, optimization, and [quantum machine learning](https://pennylane.ai/topics/quantum-machine-learning). |
Contributor
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We could probably link to a piece of optimization content!
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