Hybrid Quantum-Classical Machine Learning in TensorFlow
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TensorFlow Quantum (TFQ) is a Python framework for hybrid quantum-classical machine learning that is primarily focused on modeling quantum data. TFQ is an application framework developed to allow quantum algorithms researchers and machine learning applications researchers to explore computing workflows that leverage Google’s quantum computing offerings, all from within TensorFlow.
Quantum computing at Google has hit an exciting milestone with the achievement of Quantum Supremacy. In the wake of this demonstration, Google is now turning its attention to developing and implementing new algorithms to run on its Quantum Computer that have real world applications.
To provide users with the tools they need to program and simulate a quantum computer, Google is working on Cirq. Cirq is designed for quantum computing researchers who are interested in running and designing algorithms that leverage existing (imperfect) quantum computers.
TensorFlow Quantum provides users with the tools they need to interleave quantum algorithms and logic designed in Cirq with the powerful and performant ML tools from TensorFlow. With this connection we hope to unlock new and exciting paths for Quantum Computing research that would not have otherwise been possible.
See the installation instructions.
All of our examples can be found here in the form of Python notebook tutorials
Report bugs or feature requests using the TensorFlow Quantum issue tracker.
In the meantime check out the install instructions to get the experimental code running!
We are eager to collaborate with you! TensorFlow Quantum is still a very young code base, if you have ideas for features that you would like added feel free to check out our Contributor Guidelines to get started.
If you use TensorFlow Quantum in your research, please cite:
TensorFlow Quantum: A Software Framework for Quantum Machine Learning arXiv:2003.02989, 2020.