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https://github.com/tensorflow/quantum

An open-source Python framework for hybrid quantum-classical machine learning.
https://github.com/tensorflow/quantum

algorithms api cirq google google-quantum machine-learning machine-learning-algorithms machine-learning-library nisq python qml quantum quantum-algorithms quantum-computing quantum-information quantum-machine-learning quantum-programming quantum-simulation sdk tensorflow

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An open-source Python framework for hybrid quantum-classical machine learning.

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README 

        
![TensorFlow Quantum](./docs/images/logo/tf_quantum_circle.jpg)



---



[TensorFlow Quantum](https://www.tensorflow.org/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.



## Motivation



Quantum computing at Google has hit an exciting milestone with the achievement

of [Quantum Supremacy](https://www.nature.com/articles/s41586-019-1666-5).

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](https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html).



To provide users with the tools they need to program and simulate a quantum

computer, Google is working on [Cirq](https://github.com/quantumlib/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.



## Installation



See the [installation instructions](https://github.com/tensorflow/quantum/blob/master/docs/install.md).



## Examples



All of our examples can be found here in the form of

[Python notebook tutorials](https://github.com/tensorflow/quantum/tree/master/docs/tutorials)



## Report issues



Report bugs or feature requests using the

[TensorFlow Quantum issue tracker](https://github.com/tensorflow/quantum/issues).



We also have a [Stack Overflow tag](https://stackoverflow.com/questions/tagged/tensorflow-quantum)

for more general TFQ related discussions.



In the meantime check out the [install instructions](./docs/install.md) to get

the experimental code running!



## Contributing



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](https://github.com/tensorflow/quantum/blob/master/CONTRIBUTING.md)

to get started.



## References



If you use TensorFlow Quantum in your research, please cite:



TensorFlow Quantum: A Software Framework for Quantum Machine Learning

[arXiv:2003.02989, 2020](https://arxiv.org/abs/2003.02989).