PennyLane is a Python-based software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. It extends the automatic differentiation algorithms common in machine learning to work with quantum and hybrid computations. The library provides a unified architecture for near-term quantum computing devices, integrating with leading software platforms such as Xanadu’s Strawberry Fields, IBM’s Qiskit, Rigetti’s Forest, Google’s TensorFlow, and Facebook’s PyTorch. PennyLane can be used for variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.

Dr. Nathan Kiloran earned a Ph.D. in Physics from the Institute for Quantum Computing at the University of Waterloo, and later served as a postdoc in Machine Learning at the University of Toronto. For the past two years, he has worked at the Toronto-based quantum computing startup Xanadu, leading their software and machine learning teams.

Integrative Data Science Initiative Purdue Quantum Science Engineering Institute, The Chemistry, Physics, and Computer Science Departments at Purdue University Electrical and Computer Engineering at Purdue, Discovery Park, Entanglement Institute

Researchers should cite this work as follows:

• Nathan Killoran (2020), "PennyLane - Automatic Differentiation and Machine Learning of Quantum Computations," https://nanohub.org/resources/31597.

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1. algorithms
2. machine learning
3. quantum computing
4. quantum science and information