{"id":13776431,"url":"https://github.com/tencent-quantum-lab/tensorcircuit","last_synced_at":"2025-04-04T06:07:26.282Z","repository":{"id":37097099,"uuid":"492659956","full_name":"tencent-quantum-lab/tensorcircuit","owner":"tencent-quantum-lab","description":"Tensor network based quantum software framework for the NISQ era","archived":false,"fork":false,"pushed_at":"2024-04-10T06:24:33.000Z","size":13645,"stargazers_count":243,"open_issues_count":20,"forks_count":70,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-05-10T15:02:59.482Z","etag":null,"topics":["automatic-differentiation","jax","machine-learning","matrix-product-states","neural-network","nisq","open-quantum-systems","pytorch","quantum","quantum-algorithms","quantum-circuit","quantum-computing","quantum-dynamics","quantum-error-mitigation","quantum-machine-learning","quantum-noise","quantum-simulation","tensor-network","tensorflow","variational-quantum-algorithms"],"latest_commit_sha":null,"homepage":"https://tensorcircuit.readthedocs.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tencent-quantum-lab.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-16T02:31:33.000Z","updated_at":"2024-05-30T09:39:53.614Z","dependencies_parsed_at":"2023-10-11T05:11:18.271Z","dependency_job_id":"a5a19080-fb6f-4bd6-b922-53b97829c2e4","html_url":"https://github.com/tencent-quantum-lab/tensorcircuit","commit_stats":{"total_commits":1184,"total_committers":24,"mean_commits":"49.333333333333336","dds":"0.38344594594594594","last_synced_commit":"e1fc0614af59d93010916a05f467a6192d889d24"},"previous_names":[],"tags_count":20,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tencent-quantum-lab%2Ftensorcircuit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tencent-quantum-lab%2Ftensorcircuit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tencent-quantum-lab%2Ftensorcircuit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tencent-quantum-lab%2Ftensorcircuit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tencent-quantum-lab","download_url":"https://codeload.github.com/tencent-quantum-lab/tensorcircuit/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247128747,"owners_count":20888235,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["automatic-differentiation","jax","machine-learning","matrix-product-states","neural-network","nisq","open-quantum-systems","pytorch","quantum","quantum-algorithms","quantum-circuit","quantum-computing","quantum-dynamics","quantum-error-mitigation","quantum-machine-learning","quantum-noise","quantum-simulation","tensor-network","tensorflow","variational-quantum-algorithms"],"created_at":"2024-08-03T18:00:25.593Z","updated_at":"2025-04-04T06:07:26.256Z","avatar_url":"https://github.com/tencent-quantum-lab.png","language":"Python","funding_links":[],"categories":["Quantum full-stack libraries"],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit\"\u003e\n    \u003cimg width=90% src=\"docs/source/statics/logov2.jpg\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003c!-- tests (GitHub actions) --\u003e\n  \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/actions/workflows/ci.yml\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/actions/workflow/status/tencent-quantum-lab/tensorcircuit/ci.yml?branch=master\" /\u003e\n  \u003c/a\u003e\n  \u003c!-- docs --\u003e\n  \u003ca href=\"https://tensorcircuit.readthedocs.io/\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/docs-link-green.svg?logo=read-the-docs\"/\u003e\n  \u003c/a\u003e\n  \u003c!-- PyPI --\u003e\n  \u003ca href=\"https://pypi.org/project/tensorcircuit/\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/v/tensorcircuit.svg?logo=pypi\"/\u003e\n  \u003c/a\u003e\n  \u003c!-- binder --\u003e\n  \u003ca 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\"\u003e\n    \u003cimg src=\"https://mybinder.org/badge_logo.svg\"/\u003e\n  \u003c/a\u003e\n  \u003c!-- License --\u003e\n  \u003ca href=\"./LICENSE\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache\"/\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e English | \u003ca href=\"README_cn.md\"\u003e 简体中文 \u003c/a\u003e\u003c/p\u003e\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\u003cdetails\u003e\n  \u003csummary\u003e More highlight features for TensorCircuit (click for details) \u003c/summary\u003e\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\u003c/details\u003e\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\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: \u003c 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  \u003cdetails\u003e\n  \u003csummary\u003e Tons of amazing features and built in tools for research (click for details) \u003c/summary\u003e\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  \u003c/details\u003e\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\u003c!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --\u003e\n\u003c!-- prettier-ignore-start --\u003e\n\u003c!-- markdownlint-disable --\u003e\n\u003ctable\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://re-ra.xyz\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/35157286?v=4?s=100\" width=\"100px;\" alt=\"Shixin Zhang\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eShixin Zhang\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#example-refraction-ray\" title=\"Examples\"\u003e💡\u003c/a\u003e \u003ca href=\"#ideas-refraction-ray\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#infra-refraction-ray\" title=\"Infrastructure (Hosting, Build-Tools, etc)\"\u003e🚇\u003c/a\u003e \u003ca href=\"#maintenance-refraction-ray\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e \u003ca href=\"#research-refraction-ray\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/pulls?q=is%3Apr+reviewed-by%3Arefraction-ray\" title=\"Reviewed Pull Requests\"\u003e👀\u003c/a\u003e \u003ca href=\"#translation-refraction-ray\" title=\"Translation\"\u003e🌍\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=refraction-ray\" title=\"Tests\"\u003e⚠️\u003c/a\u003e 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\u003ca href=\"#research-SUSYUSTC\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SUSYUSTC\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/Zhouquan-Wan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/54523490?v=4?s=100\" width=\"100px;\" alt=\"Zhouquan Wan\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eZhouquan Wan\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#example-Zhouquan-Wan\" title=\"Examples\"\u003e💡\u003c/a\u003e \u003ca href=\"#ideas-Zhouquan-Wan\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#research-Zhouquan-Wan\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=Zhouquan-Wan\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"#tutorial-Zhouquan-Wan\" title=\"Tutorials\"\u003e✅\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/ls-iastu\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/70554346?v=4?s=100\" width=\"100px;\" alt=\"Shuo Liu\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eShuo Liu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#example-ls-iastu\" title=\"Examples\"\u003e💡\u003c/a\u003e \u003ca href=\"#research-ls-iastu\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"#tutorial-ls-iastu\" title=\"Tutorials\"\u003e✅\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/YHPeter\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/44126839?v=4?s=100\" width=\"100px;\" alt=\"Hao Yu\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eHao Yu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#infra-YHPeter\" title=\"Infrastructure (Hosting, Build-Tools, etc)\"\u003e🚇\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=YHPeter\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"#tutorial-YHPeter\" title=\"Tutorials\"\u003e✅\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/SexyCarrots\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/63588721?v=4?s=100\" width=\"100px;\" alt=\"Xinghan Yang\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eXinghan Yang\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=SexyCarrots\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#translation-SexyCarrots\" title=\"Translation\"\u003e🌍\u003c/a\u003e \u003ca href=\"#tutorial-SexyCarrots\" title=\"Tutorials\"\u003e✅\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/JachyMeow\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/114171061?v=4?s=100\" width=\"100px;\" alt=\"JachyMeow\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJachyMeow\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#tutorial-JachyMeow\" title=\"Tutorials\"\u003e✅\u003c/a\u003e \u003ca href=\"#translation-JachyMeow\" title=\"Translation\"\u003e🌍\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/Mzye21\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/86239031?v=4?s=100\" width=\"100px;\" alt=\"Zhaofeng Ye\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eZhaofeng Ye\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#design-Mzye21\" title=\"Design\"\u003e🎨\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/erertertet\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/41342153?v=4?s=100\" width=\"100px;\" alt=\"erertertet\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eerertertet\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=erertertet\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/yicongzheng\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/107173985?v=4?s=100\" width=\"100px;\" alt=\"Yicong Zheng\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eYicong Zheng\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#tutorial-yicongzheng\" title=\"Tutorials\"\u003e✅\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://marksong.tech\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/78847784?v=4?s=100\" width=\"100px;\" alt=\"Zixuan Song\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eZixuan Song\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#translation-MarkSong535\" title=\"Translation\"\u003e🌍\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=MarkSong535\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/buwantaiji\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/25216189?v=4?s=100\" width=\"100px;\" alt=\"Hao Xie\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eHao Xie\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=buwantaiji\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/pramitsingh0\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/52959209?v=4?s=100\" width=\"100px;\" alt=\"Pramit Singh\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003ePramit Singh\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=pramitsingh0\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/JAllcock\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/26302022?v=4?s=100\" width=\"100px;\" alt=\"Jonathan Allcock\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJonathan Allcock\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=JAllcock\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#ideas-JAllcock\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#talk-JAllcock\" title=\"Talks\"\u003e📢\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/nealchen2003\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/45502551?v=4?s=100\" width=\"100px;\" alt=\"nealchen2003\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003enealchen2003\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=nealchen2003\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/eurethia\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/84611606?v=4?s=100\" width=\"100px;\" alt=\"隐公观鱼\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003e隐公观鱼\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/WiuYuan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/108848998?v=4?s=100\" width=\"100px;\" alt=\"WiuYuan\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eWiuYuan\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#example-WiuYuan\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://www.linkedin.com/in/felix-xu-16a153196/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/61252303?v=4?s=100\" width=\"100px;\" alt=\"Felix Xu\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eFelix Xu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#tutorial-FelixXu35\" title=\"Tutorials\"\u003e✅\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://scholar.harvard.edu/hongyehu/home\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/50563225?v=4?s=100\" width=\"100px;\" alt=\"Hong-Ye Hu\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eHong-Ye Hu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=hongyehu\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/PeilinZHENG\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/45784888?v=4?s=100\" width=\"100px;\" alt=\"peilin\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003epeilin\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#tutorial-PeilinZHENG\" title=\"Tutorials\"\u003e✅\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://emilianog-byte.github.io\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/57567043?v=4?s=100\" width=\"100px;\" alt=\"Cristian Emiliano Godinez Ramirez\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eCristian Emiliano Godinez Ramirez\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/ztzhu1\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/111620128?v=4?s=100\" width=\"100px;\" alt=\"ztzhu\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eztzhu\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=ztzhu1\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/royess\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/31059422?v=4?s=100\" width=\"100px;\" alt=\"Rabqubit\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eRabqubit\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#example-royess\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/king-p3nguin\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/103920010?v=4?s=100\" width=\"100px;\" alt=\"Kazuki Tsuoka\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKazuki Tsuoka\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=king-p3nguin\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#example-king-p3nguin\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://gopal-dahale.github.io/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/49199003?v=4?s=100\" width=\"100px;\" alt=\"Gopal Ramesh Dahale\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eGopal Ramesh Dahale\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#example-Gopal-Dahale\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"16.66%\"\u003e\u003ca href=\"https://github.com/AbdullahKazi500\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/75779966?v=4?s=100\" width=\"100px;\" alt=\"Chanandellar Bong\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eChanandellar Bong\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#example-AbdullahKazi500\" title=\"Examples\"\u003e💡\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003c!-- markdownlint-restore --\u003e\n\u003c!-- prettier-ignore-end --\u003e\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:END --\u003e\n\u003c!-- prettier-ignore-start --\u003e\n\u003c!-- markdownlint-disable --\u003e\n\n\u003c!-- markdownlint-restore --\u003e\n\u003c!-- prettier-ignore-end --\u003e\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:END --\u003e\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 \u003cdetails\u003e\n  \u003csummary\u003e More research works and code projects using TensorCircuit (click for details) \u003c/summary\u003e\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  \u003c/details\u003e\n\nIf you want to highlight your research work or projects here, feel free to add by opening PR.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftencent-quantum-lab%2Ftensorcircuit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftencent-quantum-lab%2Ftensorcircuit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftencent-quantum-lab%2Ftensorcircuit/lists"}