Welcome to the official GitHub organization of TensorCircuit, hosting the high-performance quantum software framework TensorCircuit-NG, as well as the toolchains and ecosystem surrounding the TensorCircuit-NG infrastructure.
More than just a simulator, TensorCircuit-NG is built to be the foundational infrastructure for the next era of computing. In essence, it serves as the JAX for differentiable quantum programming, the CUDA for massive HPC acceleration, and the operating system (OS) for autonomous scientific agents.
- 🌐 Core Infrastructure: Learn about the TensorCircuit-NG Next-Gen Platform.
- 📝 Tech Blog: Read TensorCircuit-NG: 下一代科研基础设施 (in Chinese) by the lead creator.
- 📈 Academic Impact: Explore the growing list of peer-reviewed works citing TensorCircuit on Google Scholar.
The next-generation open-source high-performance quantum software framework.
- 🧬 Authors: Created by Shi-Xin Zhang, maintained by Shi-Xin Zhang and Yu-Qin Chen.
- 📄 Publications:
An efficient and versatile quantum computation package built on top of TensorCircuit-NG.
- 🧬 Author: Developed and maintained by Weitang Li.
- 📄 Publication:
A Rust-native Pauli algebra and structured quantum execution toolkit for TensorCircuit-NG.
- 🧬 Authors: Developed and maintained by Shi-Xin Zhang.
- 🧩 Capabilities: Pauli algebra, Hamiltonian construction, measurement grouping, symmetry reduction, stabilizer-code and error-correction analysis, structured operators, classical-shadow analysis, and observable propagation.
- 📚 Documentation: Read the TenCirPauli documentation.
A community-maintained continuation of Google's TensorNetwork library, serving as the tensor-network upstream of TensorCircuit-NG.
- 🧬 History: Originally developed by Google as TensorNetwork, which is no longer maintained. The community now continues its maintenance as part of the TensorCircuit ecosystem.
- 🧩 Capabilities: Node/edge-based tensor-network construction and contraction across NumPy, JAX, TensorFlow, and PyTorch backends.
- 📄 Publication:
- 🛠️ sxzgroup Projects: Additional research tools, training materials, and research applications.
- Auto-Grad & JIT Compatible: Native compilation on top of JAX, TensorFlow, and PyTorch for seamlessly combining quantum circuits with machine learning pipelines.
- Extreme Performance: 10 to 10^6 times faster simulation compared to conventional simulators; supports large-scale simulations up to 1000+ qubits VQE.
- QPU & HPC Native: Multi-GPU and multi-node distributed execution; supports real QPU access and advanced error mitigation.
- AI-Agent Ready: Built-in workflows and standard rules enabling AI coding assistants to write, optimize, and review quantum code autonomously.
Due to increasing interest from industry, startups, investment organizations, and research institutions, external collaborations, consulting, and advisory support are managed through a structured engagement process.
Depending on the scope and objectives, collaborations may take the form of academic exchanges, technical consultations, project support, or long-term advisory engagements.
👉 For detailed engagement options and contact information, please refer to the Collaboration & Advisory Guidelines or check the Technical Support FAQ.
