Metadata-Version: 2.1
Name: tensorcircuit-nightly
Version: 0.10.0.dev20230625
Summary: nightly release for tensorcircuit
Home-page: https://github.com/refraction-ray/tensorcircuit-dev
Author: TensorCircuit Authors
Author-email: znfesnpbh.tc@gmail.com
License: UNKNOWN
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        <p align="center"> English | <a href="README_cn.md"> 简体中文 </a></p>
        
        TensorCircuit is the next generation of quantum software framework with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.
        
        TensorCircuit is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy and approximate cases. It also supports real quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions since v0.9.
        
        ## Getting Started
        
        Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit.readthedocs.io/).
        
        For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 60+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.
        
        The following are some minimal demos.
        
        - Circuit manipulation:
        
        ```python
        import tensorcircuit as tc
        c = tc.Circuit(2)
        c.H(0)
        c.CNOT(0,1)
        c.rx(1, theta=0.2)
        print(c.wavefunction())
        print(c.expectation_ps(z=[0, 1]))
        print(c.sample(allow_state=True, batch=1024, format="count_dict_bin"))
        ```
        
        - Runtime behavior customization:
        
        ```python
        tc.set_backend("tensorflow")
        tc.set_dtype("complex128")
        tc.set_contractor("greedy")
        ```
        
        - Automatic differentiations with jit:
        
        ```python
        def forward(theta):
            c = tc.Circuit(2)
            c.R(0, theta=theta, alpha=0.5, phi=0.8)
            return tc.backend.real(c.expectation((tc.gates.z(), [0])))
        
        g = tc.backend.grad(forward)
        g = tc.backend.jit(g)
        theta = tc.array_to_tensor(1.0)
        print(g(theta))
        ```
        
        ## Install
        
        The package is written in pure Python and can be obtained via pip as:
        
        ```python
        pip install tensorcircuit
        ```
        
        We recommend you install this package with tensorflow also installed as:
        
        ```python
        pip install tensorcircuit[tensorflow]
        ```
        
        Other optional dependencies include `[torch]`, `[jax]`, `[qiskit]` and `[cloud]`.
        
        For the nightly build of tensorcircuit with new features, try:
        
        ```python
        pip uninstall tensorcircuit
        pip install tensorcircuit-nightly
        ```
        
        We also have [Docker support](/docker).
        
        ## Advantages
        
        - Tensor network simulation engine based
        
        - JIT, AD, vectorized parallelism compatible
        
        - GPU support, quantum device access support, hybrid deployment support
        
        - Efficiency
        
          - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit
        
          - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
        
        - Elegance
        
          - Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions, multiple QPU providers
        
          - API design: quantum for humans, less code, more power
        
        - Batteries included
        
          <details>
          <summary> Tons of amazing features and built in tools for research (click for details) </summary>
        
          - Support **super large circuit simulation** using tensor network engine.
        
          - Support **noisy simulation** with both Monte Carlo and density matrix (tensor network powered) modes.
        
          - Support **approximate simulation** with MPS-TEBD modes.
        
          - Support **analog/digital hybrid simulation** (time dependent Hamiltonian evolution, **pulse** level simulation) with neural ode modes.
        
          - Support **qudits simulation**.
        
          - Support **parallel** quantum circuit evaluation across **multiple GPUs**.
        
          - Highly customizable **noise model** with gate error and scalable readout error.
        
          - Support for **non-unitary** gate and post-selection simulation.
        
          - Support **real quantum devices access** from different providers.
        
          - **Scalable readout error mitigation** native to both bitstring and expectation level with automatic qubit mapping consideration.
        
          - **Advanced quantum error mitigation methods** and pipelines such as ZNE, DD, RC, etc.
        
          - Support **MPS/MPO** as representations for input states, quantum gates and observables to be measured.
        
          - Support **vectorized parallelism** on circuit inputs, circuit parameters, circuit structures, circuit measurements and these vectorization can be nested.
        
          - Gradients can be obtained with both **automatic differenation** and parameter shift (vmap accelerated) modes.
        
          - **Machine learning interface/layer/model** abstraction in both TensorFlow and PyTorch for both numerical simulation and real QPU experiments.
        
          - Circuit sampling supports both final state sampling and perfect sampling from tensor networks.
        
          - Light cone reduction support for local expectation calculation.
        
          - Highly customizable tensor network contraction path finder with opteinsum interface.
        
          - Observables are supported in measurement, sparse matrix, dense matrix and MPO format.
        
          - Super fast weighted sum Pauli string Hamiltonian matrix generation.
        
          - Reusable common circuit/measurement/problem templates and patterns.
        
          - SOTA quantum algorithm and model implementations.
        
          - Support hybrid workflows and pipelines with CPU/GPU/QPU hardware from local/cloud/hpc resources using tf/torch/jax/cupy/numpy frameoworks all at the same time.
        
          </details>
        
        ## Contributing
        
        ### Status
        
        This project is released by [Tencent Quantum Lab](https://quantum.tencent.com/) and is created and maintained by [Shi-Xin Zhang](https://github.com/refraction-ray) with current core authors [Shi-Xin Zhang](https://github.com/refraction-ray) and [Yu-Qin Chen](https://github.com/yutuer21). We also thank [contributions](https://github.com/tencent-quantum-lab/tensorcircuit/graphs/contributors) from the lab and the open source community.
        
        ### Citation
        
        If this project helps in your research, please cite our software whitepaper published in Quantum:
        
        [TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/)
        
        which is also a good introduction to the software.
        
        ### Guidelines
        
        For contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).
        
        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.
        
        ### License
        
        TensorCircuit is open source, released under the Apache License, Version 2.0.
        
        ### Contributors
        
        <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
        <!-- prettier-ignore-start -->
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        <table>
          <tbody>
            <tr>
              <td align="center" valign="top" width="16.66%"><a href="https://re-ra.xyz"><img src="https://avatars.githubusercontent.com/u/35157286?v=4?s=100" width="100px;" alt="Shixin Zhang"/><br /><sub><b>Shixin Zhang</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray" title="Documentation">📖</a> <a href="#example-refraction-ray" title="Examples">💡</a> <a href="#ideas-refraction-ray" title="Ideas, Planning, & Feedback">🤔</a> <a href="#infra-refraction-ray" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-refraction-ray" title="Maintenance">🚧</a> <a href="#research-refraction-ray" title="Research">🔬</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/pulls?q=is%3Apr+reviewed-by%3Arefraction-ray" title="Reviewed Pull Requests">👀</a> <a href="#translation-refraction-ray" title="Translation">🌍</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray" title="Tests">⚠️</a> <a href="#tutorial-refraction-ray" title="Tutorials">✅</a> <a href="#talk-refraction-ray" title="Talks">📢</a> <a href="#question-refraction-ray" title="Answering Questions">💬</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/yutuer21"><img src="https://avatars.githubusercontent.com/u/83822724?v=4?s=100" width="100px;" alt="Yuqin Chen"/><br /><sub><b>Yuqin Chen</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=yutuer21" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=yutuer21" title="Documentation">📖</a> <a href="#example-yutuer21" title="Examples">💡</a> <a href="#ideas-yutuer21" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-yutuer21" title="Research">🔬</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=yutuer21" title="Tests">⚠️</a> <a href="#tutorial-yutuer21" title="Tutorials">✅</a> <a href="#talk-yutuer21" title="Talks">📢</a></td>
              <td align="center" valign="top" width="16.66%"><a href="http://jiezhongqiu.com"><img src="https://avatars.githubusercontent.com/u/3853009?v=4?s=100" width="100px;" alt="Jiezhong Qiu"/><br /><sub><b>Jiezhong Qiu</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=xptree" title="Code">💻</a> <a href="#example-xptree" title="Examples">💡</a> <a href="#ideas-xptree" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-xptree" title="Research">🔬</a></td>
              <td align="center" valign="top" width="16.66%"><a href="http://liwt31.github.io"><img src="https://avatars.githubusercontent.com/u/22628546?v=4?s=100" width="100px;" alt="Weitang Li"/><br /><sub><b>Weitang Li</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=liwt31" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=liwt31" title="Documentation">📖</a> <a href="#ideas-liwt31" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-liwt31" title="Research">🔬</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=liwt31" title="Tests">⚠️</a> <a href="#talk-liwt31" title="Talks">📢</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/SUSYUSTC"><img src="https://avatars.githubusercontent.com/u/30529122?v=4?s=100" width="100px;" alt="Jiace Sun"/><br /><sub><b>Jiace Sun</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC" title="Documentation">📖</a> <a href="#example-SUSYUSTC" title="Examples">💡</a> <a href="#ideas-SUSYUSTC" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-SUSYUSTC" title="Research">🔬</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC" title="Tests">⚠️</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/Zhouquan-Wan"><img src="https://avatars.githubusercontent.com/u/54523490?v=4?s=100" width="100px;" alt="Zhouquan Wan"/><br /><sub><b>Zhouquan Wan</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan" title="Documentation">📖</a> <a href="#example-Zhouquan-Wan" title="Examples">💡</a> <a href="#ideas-Zhouquan-Wan" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-Zhouquan-Wan" title="Research">🔬</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan" title="Tests">⚠️</a> <a href="#tutorial-Zhouquan-Wan" title="Tutorials">✅</a></td>
            </tr>
            <tr>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/ls-iastu"><img src="https://avatars.githubusercontent.com/u/70554346?v=4?s=100" width="100px;" alt="Shuo Liu"/><br /><sub><b>Shuo Liu</b></sub></a><br /><a href="#example-ls-iastu" title="Examples">💡</a> <a href="#research-ls-iastu" title="Research">🔬</a> <a href="#tutorial-ls-iastu" title="Tutorials">✅</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/YHPeter"><img src="https://avatars.githubusercontent.com/u/44126839?v=4?s=100" width="100px;" alt="Hao Yu"/><br /><sub><b>Hao Yu</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter" title="Documentation">📖</a> <a href="#infra-YHPeter" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter" title="Tests">⚠️</a> <a href="#tutorial-YHPeter" title="Tutorials">✅</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/SexyCarrots"><img src="https://avatars.githubusercontent.com/u/63588721?v=4?s=100" width="100px;" alt="Xinghan Yang"/><br /><sub><b>Xinghan Yang</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SexyCarrots" title="Documentation">📖</a> <a href="#translation-SexyCarrots" title="Translation">🌍</a> <a href="#tutorial-SexyCarrots" title="Tutorials">✅</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/JachyMeow"><img src="https://avatars.githubusercontent.com/u/114171061?v=4?s=100" width="100px;" alt="JachyMeow"/><br /><sub><b>JachyMeow</b></sub></a><br /><a href="#tutorial-JachyMeow" title="Tutorials">✅</a> <a href="#translation-JachyMeow" title="Translation">🌍</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/Mzye21"><img src="https://avatars.githubusercontent.com/u/86239031?v=4?s=100" width="100px;" alt="Zhaofeng Ye"/><br /><sub><b>Zhaofeng Ye</b></sub></a><br /><a href="#design-Mzye21" title="Design">🎨</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/erertertet"><img src="https://avatars.githubusercontent.com/u/41342153?v=4?s=100" width="100px;" alt="erertertet"/><br /><sub><b>erertertet</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet" title="Documentation">📖</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet" title="Tests">⚠️</a></td>
            </tr>
            <tr>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/yicongzheng"><img src="https://avatars.githubusercontent.com/u/107173985?v=4?s=100" width="100px;" alt="Yicong Zheng"/><br /><sub><b>Yicong Zheng</b></sub></a><br /><a href="#tutorial-yicongzheng" title="Tutorials">✅</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://marksong.tech"><img src="https://avatars.githubusercontent.com/u/78847784?v=4?s=100" width="100px;" alt="Zixuan Song"/><br /><sub><b>Zixuan Song</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535" title="Documentation">📖</a> <a href="#translation-MarkSong535" title="Translation">🌍</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535" title="Tests">⚠️</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/buwantaiji"><img src="https://avatars.githubusercontent.com/u/25216189?v=4?s=100" width="100px;" alt="Hao Xie"/><br /><sub><b>Hao Xie</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=buwantaiji" title="Documentation">📖</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/pramitsingh0"><img src="https://avatars.githubusercontent.com/u/52959209?v=4?s=100" width="100px;" alt="Pramit Singh"/><br /><sub><b>Pramit Singh</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=pramitsingh0" title="Tests">⚠️</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/JAllcock"><img src="https://avatars.githubusercontent.com/u/26302022?v=4?s=100" width="100px;" alt="Jonathan Allcock"/><br /><sub><b>Jonathan Allcock</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=JAllcock" title="Documentation">📖</a> <a href="#ideas-JAllcock" title="Ideas, Planning, & Feedback">🤔</a> <a href="#talk-JAllcock" title="Talks">📢</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/nealchen2003"><img src="https://avatars.githubusercontent.com/u/45502551?v=4?s=100" width="100px;" alt="nealchen2003"/><br /><sub><b>nealchen2003</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=nealchen2003" title="Documentation">📖</a></td>
            </tr>
            <tr>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/eurethia"><img src="https://avatars.githubusercontent.com/u/84611606?v=4?s=100" width="100px;" alt="隐公观鱼"/><br /><sub><b>隐公观鱼</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia" title="Tests">⚠️</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://github.com/WiuYuan"><img src="https://avatars.githubusercontent.com/u/108848998?v=4?s=100" width="100px;" alt="WiuYuan"/><br /><sub><b>WiuYuan</b></sub></a><br /><a href="#example-WiuYuan" title="Examples">💡</a></td>
              <td align="center" valign="top" width="16.66%"><a href="https://www.linkedin.com/in/felix-xu-16a153196/"><img src="https://avatars.githubusercontent.com/u/61252303?v=4?s=100" width="100px;" alt="Felix Xu"/><br /><sub><b>Felix Xu</b></sub></a><br /><a href="#tutorial-FelixXu35" title="Tutorials">✅</a></td>
            </tr>
          </tbody>
        </table>
        
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        ## Research and Applications
        
        ### DQAS
        
        For the application of Differentiable Quantum Architecture Search, see [applications](/tensorcircuit/applications).
        
        Reference paper: https://arxiv.org/abs/2010.08561 (published in QST).
        
        ### VQNHE
        
        For the application of Variational Quantum-Neural Hybrid Eigensolver, see [applications](/tensorcircuit/applications).
        
        Reference paper: https://arxiv.org/abs/2106.05105 (published in PRL) and https://arxiv.org/abs/2112.10380.
        
        ### VQEX-MBL
        
        For the application of VQEX on MBL phase identification, see the [tutorial](/docs/source/tutorials/vqex_mbl.ipynb).
        
        Reference paper: https://arxiv.org/abs/2111.13719 (published in PRB).
        
        ### Stark-DTC
        
        For the numerical demosntration of discrete time crystal enabled by Stark many-body localization, see the Floquet simulation [demo](/examples/timeevolution_trotter.py).
        
        Reference paper: https://arxiv.org/abs/2208.02866 (published in PRL).
        
        ### RA-Training
        
        For the numerical simulation of variational quantum algorithm training using random gate activation strategy by us, see the [project repo](https://github.com/ls-iastu/RAtraining).
        
        Reference paper: https://arxiv.org/abs/2303.08154.
        
        ### TenCirChem
        
        [TenCirChem](https://github.com/tencent-quantum-lab/TenCirChem) is an efficient and versatile quantum computation package for molecular properties. TenCirChem is based on TensorCircuit and is optimized for chemistry applications.
        
        Reference paper: https://arxiv.org/abs/2303.10825.
        
        ### EMQAOA-DARBO
        
        For the numerical simulation and hardware experiments with error mitigation on QAOA, see the [project repo](https://github.com/sherrylixuecheng/EMQAOA-DARBO).
        
        Reference paper: https://arxiv.org/abs/2303.14877.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: tensorflow
Provides-Extra: jax
Provides-Extra: torch
Provides-Extra: qiskit
