tensorcircuit-nightly


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Version 0.12.0.dev20240618 PyPI version JSON
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home_pagehttps://github.com/refraction-ray/tensorcircuit-dev
Summarynightly release for tensorcircuit
upload_time2024-06-18 12:41:13
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authorTensorCircuit Authors
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licenseNone
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<p align="center"> English | <a href="README_cn.md"> įŽ€äŊ“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 70+ [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))
```

<details>
  <summary> More highlight features for TensorCircuit (click for details) </summary>

- Sparse Hamiltonian generation and expectation evaluation:

```python
n = 6
pauli_structures = []
weights = []
for i in range(n):
    pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i + 1) % n]}, n=n))
    weights.append(1.0)
for i in range(n):
    pauli_structures.append(tc.quantum.xyz2ps({"x": [i]}, n=n))
    weights.append(-1.0)
h = tc.quantum.PauliStringSum2COO(pauli_structures, weights)
print(h)
# BCOO(complex64[64, 64], nse=448)
c = tc.Circuit(n)
c.h(range(n))
energy = tc.templates.measurements.operator_expectation(c, h)
# -6
```

- Large-scale simulation with tensor network engine

```python
# tc.set_contractor("cotengra-30-10")
n=500
c = tc.Circuit(n)
c.h(0)
c.cx(range(n-1), range(1, n))
c.expectation_ps(z=[0, n-1], reuse=False)
```

- Density matrix simulator and quantum info quantities

```python
c = tc.DMCircuit(2)
c.h(0)
c.cx(0, 1)
c.depolarizing(1, px=0.1, py=0.1, pz=0.1)
dm = c.state()
print(tc.quantum.entropy(dm))
print(tc.quantum.entanglement_entropy(dm, [0]))
print(tc.quantum.entanglement_negativity(dm, [0]))
print(tc.quantum.log_negativity(dm, [0]))
```

</details>

## 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 **Fermion Gaussian state** simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.

  - 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.

  - Jittable classical shadow infrastructures.

  - 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 frameworks all at the same time.

  </details>

## Contributing

### Status

This project 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 open source community.

### Citation

If this project helps in your research, please cite our software whitepaper to acknowledge the work put into the development of TensorCircuit.

[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/) (published in Quantum)

which is also a good introduction to the software.

Research works citing TensorCircuit can be highlighted in [Research and Applications section](https://github.com/tencent-quantum-lab/tensorcircuit#research-and-applications).

### 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 -->
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<table>
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      <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">đŸ’ģ</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">đŸ”Ŧ</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">⚠ī¸</a> <a href="#tutorial-refraction-ray" title="Tutorials">✅</a> <a href="#talk-refraction-ray" title="Talks">đŸ“ĸ</a> <a href="#question-refraction-ray" title="Answering Questions">đŸ’Ŧ</a></td>
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      <td align="center" valign="top" width="16.66%"><a href="https://scholar.harvard.edu/hongyehu/home"><img src="https://avatars.githubusercontent.com/u/50563225?v=4?s=100" width="100px;" alt="Hong-Ye Hu"/><br /><sub><b>Hong-Ye Hu</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=hongyehu" title="Documentation">📖</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://github.com/PeilinZHENG"><img src="https://avatars.githubusercontent.com/u/45784888?v=4?s=100" width="100px;" alt="peilin"/><br /><sub><b>peilin</b></sub></a><br /><a href="#tutorial-PeilinZHENG" title="Tutorials">✅</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Code">đŸ’ģ</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Tests">⚠ī¸</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Documentation">📖</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://emilianog-byte.github.io"><img src="https://avatars.githubusercontent.com/u/57567043?v=4?s=100" width="100px;" alt="Cristian Emiliano Godinez Ramirez"/><br /><sub><b>Cristian Emiliano Godinez Ramirez</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte" title="Code">đŸ’ģ</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte" title="Tests">⚠ī¸</a></td>
    </tr>
    <tr>
      <td align="center" valign="top" width="16.66%"><a href="https://github.com/ztzhu1"><img src="https://avatars.githubusercontent.com/u/111620128?v=4?s=100" width="100px;" alt="ztzhu"/><br /><sub><b>ztzhu</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=ztzhu1" title="Code">đŸ’ģ</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://github.com/royess"><img src="https://avatars.githubusercontent.com/u/31059422?v=4?s=100" width="100px;" alt="Rabqubit"/><br /><sub><b>Rabqubit</b></sub></a><br /><a href="#example-royess" title="Examples">💡</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://github.com/king-p3nguin"><img src="https://avatars.githubusercontent.com/u/103920010?v=4?s=100" width="100px;" alt="Kazuki Tsuoka"/><br /><sub><b>Kazuki Tsuoka</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin" title="Code">đŸ’ģ</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin" title="Tests">⚠ī¸</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin" title="Documentation">📖</a> <a href="#example-king-p3nguin" title="Examples">💡</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://gopal-dahale.github.io/"><img src="https://avatars.githubusercontent.com/u/49199003?v=4?s=100" width="100px;" alt="Gopal Ramesh Dahale"/><br /><sub><b>Gopal Ramesh Dahale</b></sub></a><br /><a href="#example-Gopal-Dahale" title="Examples">💡</a></td>
      <td align="center" valign="top" width="16.66%"><a href="https://github.com/AbdullahKazi500"><img src="https://avatars.githubusercontent.com/u/75779966?v=4?s=100" width="100px;" alt="Chanandellar Bong"/><br /><sub><b>Chanandellar Bong</b></sub></a><br /><a href="#example-AbdullahKazi500" title="Examples">💡</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 (published in AQT).

### 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 (published in PRR as a Letter).

### 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 (published in JCTC).

### 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 (published in Communications Physics).

### NN-VQA

For the setup and simulation code of neural network encoded variational quantum eigensolver, see the [demo](/docs/source/tutorials/nnvqe.ipynb).

Reference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).

### More works

 <details>
  <summary> More research works and code projects using TensorCircuit (click for details) </summary>

- Neural Predictor based Quantum Architecture Search: https://arxiv.org/abs/2103.06524 (published in Machine Learning: Science and Technology).

- Quantum imaginary-time control for accelerating the ground-state preparation: https://arxiv.org/abs/2112.11782 (published in PRR).

- Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder: https://arxiv.org/abs/2301.01442 (published in PRR).

- Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics: https://arxiv.org/abs/2206.05571.

- Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461 (published in Nature Communications).

- Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation: https://arxiv.org/abs/2306.11297. Code: https://github.com/s222416822/BQFL.

- Non-IID quantum federated learning with one-shot communication complexity: https://arxiv.org/abs/2209.00768 (published in Quantum Machine Intelligence). Code: https://github.com/JasonZHM/quantum-fed-infer.

- Quantum generative adversarial imitation learning: https://doi.org/10.1088/1367-2630/acc605 (published in New Journal of Physics).

- GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381 (published in Physica A: Statistical Mechanics and its Applications).

- Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).

- Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic: https://arxiv.org/abs/2308.11616.

- Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study: https://arxiv.org/abs/2309.05924.

- Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (published in NeurIPS).

- Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866 (published in PRL).

- Quantum imaginary time evolution and quantum annealing meet topological sector optimization: https://arxiv.org/abs/2310.04291.

- Google Summer of Code 2023 Projects (QML4HEP): https://github.com/ML4SCI/QMLHEP, https://github.com/Gopal-Dahale/qgnn-hep, https://github.com/salcc/QuantumTransformers.

- Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393 (published in PRL).

- Non-Markovianity benefits quantum dynamics simulation: https://arxiv.org/abs/2311.17622.

  </details>

If you want to highlight your research work or projects here, feel free to add by opening PR.

            

Raw data

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    "home_page": "https://github.com/refraction-ray/tensorcircuit-dev",
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    "author": "TensorCircuit Authors",
    "author_email": "znfesnpbh.tc@gmail.com",
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    "platform": null,
    "description": "<p align=\"center\">\n  <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit\">\n    <img width=90% src=\"docs/source/statics/logov2.jpg\">\n  </a>\n</p>\n\n<p align=\"center\">\n  <!-- tests (GitHub actions) -->\n  <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/actions/workflows/ci.yml\">\n    <img src=\"https://img.shields.io/github/actions/workflow/status/tencent-quantum-lab/tensorcircuit/ci.yml?branch=master\" />\n  </a>\n  <!-- docs -->\n  <a href=\"https://tensorcircuit.readthedocs.io/\">\n    <img src=\"https://img.shields.io/badge/docs-link-green.svg?logo=read-the-docs\"/>\n  </a>\n  <!-- PyPI -->\n  <a href=\"https://pypi.org/project/tensorcircuit/\">\n    <img src=\"https://img.shields.io/pypi/v/tensorcircuit.svg?logo=pypi\"/>\n  </a>\n  <!-- binder -->\n  <a href=\"https://mybinder.org/v2/gh/refraction-ray/tc-env/master?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Ftencent-quantum-lab%252Ftensorcircuit%26urlpath%3Dlab%252Ftree%252Ftensorcircuit%252F%26branch%3Dmaster\">\n    <img src=\"https://mybinder.org/badge_logo.svg\"/>\n  </a>\n  <!-- License -->\n  <a href=\"./LICENSE\">\n    <img src=\"https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache\"/>\n  </a>\n</p>\n\n<p align=\"center\"> English | <a href=\"README_cn.md\"> \u7b80\u4f53\u4e2d\u6587 </a></p>\n\nTensorCircuit is the next generation of quantum software framework with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.\n\nTensorCircuit 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.\n\n## Getting Started\n\nPlease begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit.readthedocs.io/).\n\nFor more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 70+ [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.\n\nThe following are some minimal demos.\n\n- Circuit manipulation:\n\n```python\nimport tensorcircuit as tc\nc = tc.Circuit(2)\nc.H(0)\nc.CNOT(0,1)\nc.rx(1, theta=0.2)\nprint(c.wavefunction())\nprint(c.expectation_ps(z=[0, 1]))\nprint(c.sample(allow_state=True, batch=1024, format=\"count_dict_bin\"))\n```\n\n- Runtime behavior customization:\n\n```python\ntc.set_backend(\"tensorflow\")\ntc.set_dtype(\"complex128\")\ntc.set_contractor(\"greedy\")\n```\n\n- Automatic differentiations with jit:\n\n```python\ndef forward(theta):\n    c = tc.Circuit(2)\n    c.R(0, theta=theta, alpha=0.5, phi=0.8)\n    return tc.backend.real(c.expectation((tc.gates.z(), [0])))\n\ng = tc.backend.grad(forward)\ng = tc.backend.jit(g)\ntheta = tc.array_to_tensor(1.0)\nprint(g(theta))\n```\n\n<details>\n  <summary> More highlight features for TensorCircuit (click for details) </summary>\n\n- Sparse Hamiltonian generation and expectation evaluation:\n\n```python\nn = 6\npauli_structures = []\nweights = []\nfor i in range(n):\n    pauli_structures.append(tc.quantum.xyz2ps({\"z\": [i, (i + 1) % n]}, n=n))\n    weights.append(1.0)\nfor i in range(n):\n    pauli_structures.append(tc.quantum.xyz2ps({\"x\": [i]}, n=n))\n    weights.append(-1.0)\nh = tc.quantum.PauliStringSum2COO(pauli_structures, weights)\nprint(h)\n# BCOO(complex64[64, 64], nse=448)\nc = tc.Circuit(n)\nc.h(range(n))\nenergy = tc.templates.measurements.operator_expectation(c, h)\n# -6\n```\n\n- Large-scale simulation with tensor network engine\n\n```python\n# tc.set_contractor(\"cotengra-30-10\")\nn=500\nc = tc.Circuit(n)\nc.h(0)\nc.cx(range(n-1), range(1, n))\nc.expectation_ps(z=[0, n-1], reuse=False)\n```\n\n- Density matrix simulator and quantum info quantities\n\n```python\nc = tc.DMCircuit(2)\nc.h(0)\nc.cx(0, 1)\nc.depolarizing(1, px=0.1, py=0.1, pz=0.1)\ndm = c.state()\nprint(tc.quantum.entropy(dm))\nprint(tc.quantum.entanglement_entropy(dm, [0]))\nprint(tc.quantum.entanglement_negativity(dm, [0]))\nprint(tc.quantum.log_negativity(dm, [0]))\n```\n\n</details>\n\n## Install\n\nThe package is written in pure Python and can be obtained via pip as:\n\n```python\npip install tensorcircuit\n```\n\nWe recommend you install this package with tensorflow also installed as:\n\n```python\npip install tensorcircuit[tensorflow]\n```\n\nOther optional dependencies include `[torch]`, `[jax]`, `[qiskit]` and `[cloud]`.\n\nFor the nightly build of tensorcircuit with new features, try:\n\n```python\npip uninstall tensorcircuit\npip install tensorcircuit-nightly\n```\n\nWe also have [Docker support](/docker).\n\n## Advantages\n\n- Tensor network simulation engine based\n\n- JIT, AD, vectorized parallelism compatible\n\n- GPU support, quantum device access support, hybrid deployment support\n\n- Efficiency\n\n  - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit\n\n  - Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)\n\n- Elegance\n\n  - Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions, multiple QPU providers\n\n  - API design: quantum for humans, less code, more power\n\n- Batteries included\n\n  <details>\n  <summary> Tons of amazing features and built in tools for research (click for details) </summary>\n\n  - Support **super large circuit simulation** using tensor network engine.\n\n  - Support **noisy simulation** with both Monte Carlo and density matrix (tensor network powered) modes.\n\n  - Support **approximate simulation** with MPS-TEBD modes.\n\n  - Support **analog/digital hybrid simulation** (time dependent Hamiltonian evolution, **pulse** level simulation) with neural ode modes.\n\n  - Support **Fermion Gaussian state** simulation with expectation, entanglement, measurement, ground state, real and imaginary time evolution.\n\n  - Support **qudits simulation**.\n\n  - Support **parallel** quantum circuit evaluation across **multiple GPUs**.\n\n  - Highly customizable **noise model** with gate error and scalable readout error.\n\n  - Support for **non-unitary** gate and post-selection simulation.\n\n  - Support **real quantum devices access** from different providers.\n\n  - **Scalable readout error mitigation** native to both bitstring and expectation level with automatic qubit mapping consideration.\n\n  - **Advanced quantum error mitigation methods** and pipelines such as ZNE, DD, RC, etc.\n\n  - Support **MPS/MPO** as representations for input states, quantum gates and observables to be measured.\n\n  - Support **vectorized parallelism** on circuit inputs, circuit parameters, circuit structures, circuit measurements and these vectorization can be nested.\n\n  - Gradients can be obtained with both **automatic differenation** and parameter shift (vmap accelerated) modes.\n\n  - **Machine learning interface/layer/model** abstraction in both TensorFlow and PyTorch for both numerical simulation and real QPU experiments.\n\n  - Circuit sampling supports both final state sampling and perfect sampling from tensor networks.\n\n  - Light cone reduction support for local expectation calculation.\n\n  - Highly customizable tensor network contraction path finder with opteinsum interface.\n\n  - Observables are supported in measurement, sparse matrix, dense matrix and MPO format.\n\n  - Super fast weighted sum Pauli string Hamiltonian matrix generation.\n\n  - Reusable common circuit/measurement/problem templates and patterns.\n\n  - Jittable classical shadow infrastructures.\n\n  - SOTA quantum algorithm and model implementations.\n\n  - Support hybrid workflows and pipelines with CPU/GPU/QPU hardware from local/cloud/hpc resources using tf/torch/jax/cupy/numpy frameworks all at the same time.\n\n  </details>\n\n## Contributing\n\n### Status\n\nThis project 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 open source community.\n\n### Citation\n\nIf this project helps in your research, please cite our software whitepaper to acknowledge the work put into the development of TensorCircuit.\n\n[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/) (published in Quantum)\n\nwhich is also a good introduction to the software.\n\nResearch works citing TensorCircuit can be highlighted in [Research and Applications section](https://github.com/tencent-quantum-lab/tensorcircuit#research-and-applications).\n\n### Guidelines\n\nFor contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).\n\nWe 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.\n\n### License\n\nTensorCircuit is open source, released under the Apache License, Version 2.0.\n\n### Contributors\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n<table>\n  <tbody>\n    <tr>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#example-refraction-ray\" title=\"Examples\">\ud83d\udca1</a> <a href=\"#ideas-refraction-ray\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#infra-refraction-ray\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"#maintenance-refraction-ray\" title=\"Maintenance\">\ud83d\udea7</a> <a href=\"#research-refraction-ray\" title=\"Research\">\ud83d\udd2c</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/pulls?q=is%3Apr+reviewed-by%3Arefraction-ray\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a> <a href=\"#translation-refraction-ray\" title=\"Translation\">\ud83c\udf0d</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#tutorial-refraction-ray\" title=\"Tutorials\">\u2705</a> <a href=\"#talk-refraction-ray\" title=\"Talks\">\ud83d\udce2</a> <a href=\"#question-refraction-ray\" title=\"Answering Questions\">\ud83d\udcac</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=yutuer21\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#example-yutuer21\" title=\"Examples\">\ud83d\udca1</a> <a href=\"#ideas-yutuer21\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#research-yutuer21\" title=\"Research\">\ud83d\udd2c</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=yutuer21\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#tutorial-yutuer21\" title=\"Tutorials\">\u2705</a> <a href=\"#talk-yutuer21\" title=\"Talks\">\ud83d\udce2</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"#example-xptree\" title=\"Examples\">\ud83d\udca1</a> <a href=\"#ideas-xptree\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#research-xptree\" title=\"Research\">\ud83d\udd2c</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=liwt31\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#ideas-liwt31\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#research-liwt31\" title=\"Research\">\ud83d\udd2c</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=liwt31\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#talk-liwt31\" title=\"Talks\">\ud83d\udce2</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#example-SUSYUSTC\" title=\"Examples\">\ud83d\udca1</a> <a href=\"#ideas-SUSYUSTC\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#research-SUSYUSTC\" title=\"Research\">\ud83d\udd2c</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC\" title=\"Tests\">\u26a0\ufe0f</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#example-Zhouquan-Wan\" title=\"Examples\">\ud83d\udca1</a> <a href=\"#ideas-Zhouquan-Wan\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#research-Zhouquan-Wan\" title=\"Research\">\ud83d\udd2c</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#tutorial-Zhouquan-Wan\" title=\"Tutorials\">\u2705</a></td>\n    </tr>\n    <tr>\n      <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\">\ud83d\udca1</a> <a href=\"#research-ls-iastu\" title=\"Research\">\ud83d\udd2c</a> <a href=\"#tutorial-ls-iastu\" title=\"Tutorials\">\u2705</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#infra-YHPeter\" title=\"Infrastructure (Hosting, Build-Tools, etc)\">\ud83d\ude87</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"#tutorial-YHPeter\" title=\"Tutorials\">\u2705</a></td>\n      <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\">\ud83d\udcd6</a> <a href=\"#translation-SexyCarrots\" title=\"Translation\">\ud83c\udf0d</a> <a href=\"#tutorial-SexyCarrots\" title=\"Tutorials\">\u2705</a></td>\n      <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\">\u2705</a> <a href=\"#translation-JachyMeow\" title=\"Translation\">\ud83c\udf0d</a></td>\n      <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\">\ud83c\udfa8</a></td>\n      <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\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet\" title=\"Tests\">\u26a0\ufe0f</a></td>\n    </tr>\n    <tr>\n      <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\">\u2705</a></td>\n      <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\">\ud83d\udcd6</a> <a href=\"#translation-MarkSong535\" title=\"Translation\">\ud83c\udf0d</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535\" title=\"Tests\">\u26a0\ufe0f</a></td>\n      <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\">\ud83d\udcd6</a></td>\n      <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\">\u26a0\ufe0f</a></td>\n      <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\">\ud83d\udcd6</a> <a href=\"#ideas-JAllcock\" title=\"Ideas, Planning, & Feedback\">\ud83e\udd14</a> <a href=\"#talk-JAllcock\" title=\"Talks\">\ud83d\udce2</a></td>\n      <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\">\ud83d\udcd6</a></td>\n    </tr>\n    <tr>\n      <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=\"\u9690\u516c\u89c2\u9c7c\"/><br /><sub><b>\u9690\u516c\u89c2\u9c7c</b></sub></a><br /><a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia\" title=\"Tests\">\u26a0\ufe0f</a></td>\n      <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\">\ud83d\udca1</a></td>\n      <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\">\u2705</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35\" title=\"Tests\">\u26a0\ufe0f</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://scholar.harvard.edu/hongyehu/home\"><img src=\"https://avatars.githubusercontent.com/u/50563225?v=4?s=100\" width=\"100px;\" alt=\"Hong-Ye Hu\"/><br /><sub><b>Hong-Ye Hu</b></sub></a><br /><a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=hongyehu\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://github.com/PeilinZHENG\"><img src=\"https://avatars.githubusercontent.com/u/45784888?v=4?s=100\" width=\"100px;\" alt=\"peilin\"/><br /><sub><b>peilin</b></sub></a><br /><a href=\"#tutorial-PeilinZHENG\" title=\"Tutorials\">\u2705</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Documentation\">\ud83d\udcd6</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://emilianog-byte.github.io\"><img src=\"https://avatars.githubusercontent.com/u/57567043?v=4?s=100\" width=\"100px;\" alt=\"Cristian Emiliano Godinez Ramirez\"/><br /><sub><b>Cristian Emiliano Godinez Ramirez</b></sub></a><br /><a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte\" title=\"Tests\">\u26a0\ufe0f</a></td>\n    </tr>\n    <tr>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://github.com/ztzhu1\"><img src=\"https://avatars.githubusercontent.com/u/111620128?v=4?s=100\" width=\"100px;\" alt=\"ztzhu\"/><br /><sub><b>ztzhu</b></sub></a><br /><a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=ztzhu1\" title=\"Code\">\ud83d\udcbb</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://github.com/royess\"><img src=\"https://avatars.githubusercontent.com/u/31059422?v=4?s=100\" width=\"100px;\" alt=\"Rabqubit\"/><br /><sub><b>Rabqubit</b></sub></a><br /><a href=\"#example-royess\" title=\"Examples\">\ud83d\udca1</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://github.com/king-p3nguin\"><img src=\"https://avatars.githubusercontent.com/u/103920010?v=4?s=100\" width=\"100px;\" alt=\"Kazuki Tsuoka\"/><br /><sub><b>Kazuki Tsuoka</b></sub></a><br /><a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Code\">\ud83d\udcbb</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Tests\">\u26a0\ufe0f</a> <a href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Documentation\">\ud83d\udcd6</a> <a href=\"#example-king-p3nguin\" title=\"Examples\">\ud83d\udca1</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://gopal-dahale.github.io/\"><img src=\"https://avatars.githubusercontent.com/u/49199003?v=4?s=100\" width=\"100px;\" alt=\"Gopal Ramesh Dahale\"/><br /><sub><b>Gopal Ramesh Dahale</b></sub></a><br /><a href=\"#example-Gopal-Dahale\" title=\"Examples\">\ud83d\udca1</a></td>\n      <td align=\"center\" valign=\"top\" width=\"16.66%\"><a href=\"https://github.com/AbdullahKazi500\"><img src=\"https://avatars.githubusercontent.com/u/75779966?v=4?s=100\" width=\"100px;\" alt=\"Chanandellar Bong\"/><br /><sub><b>Chanandellar Bong</b></sub></a><br /><a href=\"#example-AbdullahKazi500\" title=\"Examples\">\ud83d\udca1</a></td>\n    </tr>\n  </tbody>\n</table>\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n\n<!-- markdownlint-restore -->\n<!-- prettier-ignore-end -->\n\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\n## Research and Applications\n\n### DQAS\n\nFor the application of Differentiable Quantum Architecture Search, see [applications](/tensorcircuit/applications).\n\nReference paper: https://arxiv.org/abs/2010.08561 (published in QST).\n\n### VQNHE\n\nFor the application of Variational Quantum-Neural Hybrid Eigensolver, see [applications](/tensorcircuit/applications).\n\nReference paper: https://arxiv.org/abs/2106.05105 (published in PRL) and https://arxiv.org/abs/2112.10380 (published in AQT).\n\n### VQEX-MBL\n\nFor the application of VQEX on MBL phase identification, see the [tutorial](/docs/source/tutorials/vqex_mbl.ipynb).\n\nReference paper: https://arxiv.org/abs/2111.13719 (published in PRB).\n\n### Stark-DTC\n\nFor the numerical demosntration of discrete time crystal enabled by Stark many-body localization, see the Floquet simulation [demo](/examples/timeevolution_trotter.py).\n\nReference paper: https://arxiv.org/abs/2208.02866 (published in PRL).\n\n### RA-Training\n\nFor 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).\n\nReference paper: https://arxiv.org/abs/2303.08154 (published in PRR as a Letter).\n\n### TenCirChem\n\n[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.\n\nReference paper: https://arxiv.org/abs/2303.10825 (published in JCTC).\n\n### EMQAOA-DARBO\n\nFor the numerical simulation and hardware experiments with error mitigation on QAOA, see the [project repo](https://github.com/sherrylixuecheng/EMQAOA-DARBO).\n\nReference paper: https://arxiv.org/abs/2303.14877 (published in Communications Physics).\n\n### NN-VQA\n\nFor the setup and simulation code of neural network encoded variational quantum eigensolver, see the [demo](/docs/source/tutorials/nnvqe.ipynb).\n\nReference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).\n\n### More works\n\n <details>\n  <summary> More research works and code projects using TensorCircuit (click for details) </summary>\n\n- Neural Predictor based Quantum Architecture Search: https://arxiv.org/abs/2103.06524 (published in Machine Learning: Science and Technology).\n\n- Quantum imaginary-time control for accelerating the ground-state preparation: https://arxiv.org/abs/2112.11782 (published in PRR).\n\n- Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder: https://arxiv.org/abs/2301.01442 (published in PRR).\n\n- Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics: https://arxiv.org/abs/2206.05571.\n\n- Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461 (published in Nature Communications).\n\n- Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation: https://arxiv.org/abs/2306.11297. Code: https://github.com/s222416822/BQFL.\n\n- Non-IID quantum federated learning with one-shot communication complexity: https://arxiv.org/abs/2209.00768 (published in Quantum Machine Intelligence). Code: https://github.com/JasonZHM/quantum-fed-infer.\n\n- Quantum generative adversarial imitation learning: https://doi.org/10.1088/1367-2630/acc605 (published in New Journal of Physics).\n\n- GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381 (published in Physica A: Statistical Mechanics and its Applications).\n\n- Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).\n\n- Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic: https://arxiv.org/abs/2308.11616.\n\n- Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study: https://arxiv.org/abs/2309.05924.\n\n- Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (published in NeurIPS).\n\n- Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866 (published in PRL).\n\n- Quantum imaginary time evolution and quantum annealing meet topological sector optimization: https://arxiv.org/abs/2310.04291.\n\n- Google Summer of Code 2023 Projects (QML4HEP): https://github.com/ML4SCI/QMLHEP, https://github.com/Gopal-Dahale/qgnn-hep, https://github.com/salcc/QuantumTransformers.\n\n- Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393 (published in PRL).\n\n- Non-Markovianity benefits quantum dynamics simulation: https://arxiv.org/abs/2311.17622.\n\n  </details>\n\nIf you want to highlight your research work or projects here, feel free to add by opening PR.\n",
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