Metadata-Version: 2.1
Name: tensorcircuit-nightly
Version: 0.6.0.dev20230102
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 circuit simulators with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.
        
        TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.
        
        ## Getting Started
        
        Please begin with [Quick Start](/docs/source/quickstart.rst).
        
        For more information and introductions, please refer to helpful [example scripts](/examples) and [full documentation](https://tensorcircuit.readthedocs.io/). 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())
        ```
        
        - 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]` and `[qiskit]`.
        
        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
        
        - 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
        
          - API design: quantum for humans, less code, more power
        
        ## Citing TensorCircuit
        
        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.
        
        If this project helps in your research, please cite our software whitepaper:
        
        [TensorCircuit: a Quantum Software Framework for the NISQ Era](https://arxiv.org/abs/2205.10091)
        
        which is also a good introduction to the software.
        
        ## Contributing
        
        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.
        
        ## Research and Applications
        
        ### DQAS
        
        For the application of Differentiable Quantum Architecture Search, see [applications](/tensorcircuit/applications).
        Reference paper: https://arxiv.org/pdf/2010.08561.pdf.
        
        ### VQNHE
        
        For the application of Variational Quantum-Neural Hybrid Eigensolver, see [applications](/tensorcircuit/applications).
        Reference paper: https://arxiv.org/pdf/2106.05105.pdf and https://arxiv.org/pdf/2112.10380.pdf.
        
        ### 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/pdf/2111.13719.pdf.
        
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
