Contents

pennylane 0.44.0

0

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and qu

PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.

Stars: 3079, Watchers: 3079, Forks: 744, Open Issues: 446

The PennyLaneAI/pennylane repo was created 7 years ago and the last code push was 41 minutes ago.
The project is very popular with an impressive 3079 github stars!

How to Install pennylane

You can install pennylane using pip

pip install pennylane

or add it to a project with poetry

poetry add pennylane

Package Details

Author
None
License
None
Homepage
None
PyPi:
https://pypi.org/project/pennylane/
GitHub Repo:
https://github.com/XanaduAI/pennylane

Classifiers

  • Scientific/Engineering/Physics
No  pennylane  pypi packages just yet.

Errors

A list of common pennylane errors.

Code Examples

Here are some pennylane code examples and snippets.

GitHub Issues

The pennylane package has 446 open issues on GitHub

  • [WIP] Deprecate the BoundTransform.transform
  • Improve for_loop error messages
  • Add weights to PPRs and PPMs in specs output
  • Sort and add binary linear algebra functionality to qml.math
  • feat: add get_compile_pipeline to workflow module
  • Scope deprecation warnings
  • [tach] enforce public interface of workflow module
  • [WIP] create mark(op, label) function to replace id functionality
  • Miscellaneous graph-decomposition related fixes
  • Implement Sum of Slaters state preparation
  • OpenQasm support for ParameterizedEvolution
  • [WIP] experiment: add deprecations.py and helper functions
  • Resolve solution-not-found from DecompositionGraph
  • deprecate: id kwarg from Operator
  • [BUG] BasisState on default.clifford triggers dense state allocation (fails at 64 qubits)

See more issues on GitHub

Related Packages & Articles

onnx 1.20.1

Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Currently we focus on the capabilities needed for inferencing (scoring).

transformers 5.2.0

Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

thinc 9.1.1

A refreshing functional take on deep learning, compatible with your favorite libraries

keras 3.13.2

Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. The core data structures of Keras are layers and models. The philosophy is to keep simple things simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code via subclassing).

petastorm 0.13.1

Petastorm is a library enabling the use of Parquet storage from Tensorflow, Pytorch, and other Python-based ML training frameworks.

kornia 0.8.2

Open Source Differentiable Computer Vision Library for PyTorch

horovod 0.28.1

Horovod is a powerful distributed training framework for Python that allows you to train deep learning models across multiple GPUs and servers quickly and efficiently. It falls under the category of distributed computing libraries. Built on top of TensorFlow, PyTorch, and other popular deep learning frameworks, Horovod simplifies the process of scaling up your model training by handling the complexities of distributed training under the hood.