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Update README.md
Fixed README typos.
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README.md

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## Install
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The package is purely written in Python and can be obtained via pip as:
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The package is written in pure Python and can be obtained via pip as:
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```python
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pip install tensorcircuit
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```
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And we recommend you install this package with tensorflow also installed as:
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We recommend you install this package with tensorflow also installed as:
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```python
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pip install tensorcircuit[tensorflow]
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```
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Other optional dependencies include `[torch]`, `[jax]` and `[qiskit]`.
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For nightly build of tensorcircuit with new features, try:
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For the nightly build of tensorcircuit with new features, try:
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```python
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pip uninstall tensorcircuit
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## Citing TensorCircuit
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This project is released by [Tencent Quantum Lab](https://quantum.tencent.com/) and is currently maintained by [Shi-Xin Zhang](https://github.com/refraction-ray) with contributions from the lab and open source community.
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This project is released by [Tencent Quantum Lab](https://quantum.tencent.com/) and is currently maintained by [Shi-Xin Zhang](https://github.com/refraction-ray) with contributions from the lab and the open source community.
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If this project helps in your research, please cite our software whitepaper:
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[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://arxiv.org/abs/2205.10091)
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which is also a good introduction for the software.
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which is also a good introduction to the software.
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## Contributing
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For contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).
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We welcome [issues](https://github.com/tencent-quantum-lab/tensorcircuit/issues), [PRs](https://github.com/tencent-quantum-lab/tensorcircuit/pulls), and [discussions](https://github.com/tencent-quantum-lab/tensorcircuit/discussions) from everyone, and these are all hosted on GitHub.
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## Researches and Applications
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## Research and Applications
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### DQAS
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