Welcome and congratulations! You have found TensorCircuit. 👏
TensorCircuit is an open-source high-performance quantum computing software framework in Python.
It is built for humans. 👽
It is designed for speed, flexibility and elegance. 🚀
It is empowered by advanced tensor network simulator engine. 🔋
It is ready for quantum hardware access with CPU/GPU/QPU (local/cloud) hybrid solutions. 🖥
It is implemented with industry-standard machine learning frameworks: TensorFlow, JAX, and PyTorch. 🤖
It is compatible with machine learning engineering paradigms: automatic differentiation, just-in-time compilation, vectorized parallelism and GPU acceleration. 🛠
With the help of TensorCircuit, now get ready to efficiently and elegantly solve interesting and challenging quantum computing problems: from academic research prototype to industry application deployment.
Unified Quantum Programming#
TensorCircuit is unifying infrastructures and interfaces for quantum computing.
QPUs from different vendors
numerical sim/hardware exp
stateless functional programming/stateful ML models
The following documentation sections briefly introduce TensorCircuit to the users and developpers.
- Quick Start
- Advanced Usage
- Frequently Asked Questions
- How can I run TensorCircuit on GPU?
- When should I use GPU for the quantum simulation?
- When should I jit the function?
- Which ML framework backend should I use?
- What is the counterpart of
QuantumLayerfor PyTorch and Jax backend?
- When do I need to customize the contractor and how?
- Is there some API less cumbersome than
expectationfor Pauli string?
- Can I apply quantum operation based on previous classical measurement results in TensorCircuit?
- How to understand the difference between different measurement methods for
- How to understand difference between
- How to arrange the circuit gate placement in the visualization from
- How to get the entanglement entropy from the circuit output?
- TensorCircuit: The Sharp Bits 🔪
- TensorCircuit: What is inside?
- Guide for Contributors
The following documentation sections include integrated examples in the form of Jupyter Notebook.
- Jupyter Tutorials
- Circuit Basics
- Quantum Approximation Optimization Algorithm (QAOA)
- Optimizing QAOA by Bayesian Optimization (BO)
- Quantum Approximation Optimization Algorithm (QAOA) for Not-all-equal 3-satisfiability (NAE3SAT)
- Quantum Dropout for QAOA
- VQE on 1D TFIM
- QML on MNIST Classification
- QML in PyTorch
- Quantum Machine Learning for Classification Task
- Variational Quantum Eigensolver (VQE) on Molecules
- VQE on 1D TFIM with Different Hamiltonian Representation
- Gradient Evaluation Efficiency Comparison
- The usage of contractor
- Operator spreading
- Optimization vs. expressibility of the circuit
- Probing Many-body Localization by Excited-state VQE
- Differentiable Quantum Architecture Search
- Barren Plateaus
- Solving QUBO Problem using QAOA
- Portfolio Optimization
- Solving the Ground State of Hamiltonian by Imaginary-time Evolution
- Classical Shadows in Pauli Basis
- Support Vector Classification with SKLearn
- Demo on TensorCircuit SDK for Tencent Quantum Cloud
- Whitepaper Tutorials