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Merge pull request #241 from mit-han-lab/dev
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README.md

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Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.
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## News
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- v0.1.8 Available!
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- Check the [dev branch](https://github.com/mit-han-lab/torchquantum/tree/dev) for new latest features on quantum layers and quantum algorithms.
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- v0.1.7 Available!
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- Join our [Slack](https://join.slack.com/t/torchquantum/shared_invite/zt-1ghuf283a-OtP4mCPJREd~367VX~TaQQ) for real time support!
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- Welcome to contribute! Please contact us or post in the Github Issues if you want to have new examples implemented by TorchQuantum or any other questions.
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- Qmlsys website goes online: [qmlsys.mit.edu](https://qmlsys.mit.edu) and [torchquantum.org](https://torchquantum.org)

setup.py

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setup(
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name="torchquantum",
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version=VERSION["version"],
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description="A PyTorch-based framework for differentiable classical simulation of quantum computing",
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description="Quantum Computing in PyTorch",
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url="https://github.com/mit-han-lab/torchquantum",
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author="Hanrui Wang, Jiannan Cao, Jessica Ding, Jiai Gu, Song Han, Zirui Li, Zhiding Liang, Pengyu Liu, Mohammadreza Tavasoli",
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author="Shreya Chaudhary, Zhuoyang Ye, Jiannan Cao, Jessica Ding, Jiai Gu, Song Han, Zirui Li, Zhiding Liang, Pengyu Liu, Mohammadreza Tavasoli, Hanrui Wang",
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author_email="hanruiwang.hw@gmail.com",
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license="MIT",
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install_requires=requirements,

torchquantum/__version__.py

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# packages=find_packages(),
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# )
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version = "0.1.7"
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version = "0.1.8"

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