TensorCircuit Documentation

Welcome and congratulations! You have found TensorCircuit. 👏

Introduction

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.

Relevant Links

TensorCircuit is created and maintained by Shi-Xin Zhang and this version is released by Tencent Quantum Lab.

The current core authors of TensorCircuit are Shi-Xin Zhang and Yu-Qin Chen. We also thank contributions from the open source community.

If you have any further questions or collaboration ideas, please use the issue tracker or forum below, or send email to shixinzhang#tencent.com.

Source code

GitHub

Documentation

Readthedocs

Whitepaper

Quantum journal

Issue Tracker

GitHub Issues

Forum

GitHub Discussions

PyPI

pip install

DockerHub

docker pull

Application

Research using TC

Cloud

Tencent Quantum Cloud

Unified Quantum Programming

TensorCircuit is unifying infrastructures and interfaces for quantum computing.

Unified Backends

Jax/TensorFlow/PyTorch/Numpy/Cupy

Unified Devices

CPU/GPU/TPU

Unified Providers

QPUs from different vendors

Unified Resources

local/cloud/HPC

Unified Interfaces

numerical sim/hardware exp

Unified Engines

ideal/noisy/approximate simulation

Unified Representations

from/to_IR/qiskit/openqasm/json

Unified Pipelines

stateless functional programming/stateful ML models

Reference Documentation

The following documentation sections briefly introduce TensorCircuit to the users and developpers.

• Quick Start
  • Installation
  • Circuit Object
  • Programming Paradigm
  • Automatic Differentiation, JIT, and Vectorized Parallelism
  • Backend Agnosticism
  • Switch the Dtype
  • Setup the Contractor
  • Noisy Circuit Simulation
  • MPS and MPO
  • Interfaces
  • Templates as Shortcuts
• Advanced Usage
  • MPS Simulator
  • Split Two-qubit Gates
  • Jitted Function Save/Load
  • Parameterized Measurements
  • Sparse Matrix
  • Randoms, Jit, Backend Agnostic, and Their Interplay
• 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 QuantumLayer for PyTorch and Jax backend?
  • When do I need to customize the contractor and how?
  • Is there some API less cumbersome than expectation for 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 Circuit?
  • How to understand difference between tc.array_to_tensor and tc.backend.convert_to_tensor?
  • How to arrange the circuit gate placement in the visualization from c.tex()?
  • How to get the entanglement entropy from the circuit output?
• TensorCircuit: The Sharp Bits 🔪
  • Jit Compatibility
  • Tensor variables consistency
  • AD Consistency
• TensorCircuit: What is inside?
  • Overview of Modules
  • Relation between TensorCircuit and TensorNetwork
  • Relations of Circuit-like classes
  • QuOperator/QuVector and MPO/MPS
  • Quantum Cloud SDK: Layerwise API design
• Guide for Contributors
  • Pull Request Guidelines
  • Checks
  • Pytest
  • Docs
  • Releases

Tutorials

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
  • MERA
  • 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
  • Circuits and Gates
  • Gradient and Variational Optimization
  • Density Matrix and Mixed State Evolution
  • Different Types of Measurement API
  • Evaluation on Pauli String Sum
  • Utilizing vmap in Quantum Circuit Simulations
  • QuOperator in TensorCircuit
  • Customized Contraction
  • Advanced Automatic Differentiation

API References

• tensorcircuit
  • tensorcircuit.about
  • tensorcircuit.abstractcircuit
  • tensorcircuit.applications
  • tensorcircuit.backends
  • tensorcircuit.basecircuit
  • tensorcircuit.channels
  • tensorcircuit.circuit
  • tensorcircuit.cloud
  • tensorcircuit.compiler
  • tensorcircuit.cons
  • tensorcircuit.densitymatrix
  • tensorcircuit.experimental
  • tensorcircuit.fgs
  • tensorcircuit.gates
  • tensorcircuit.interfaces
  • tensorcircuit.keras
  • tensorcircuit.mps_base
  • tensorcircuit.mpscircuit
  • tensorcircuit.noisemodel
  • tensorcircuit.quantum
  • tensorcircuit.results
  • tensorcircuit.shadows
  • tensorcircuit.simplify
  • tensorcircuit.templates
  • tensorcircuit.torchnn
  • tensorcircuit.translation
  • tensorcircuit.utils
  • tensorcircuit.vis

Indices and Tables

• Index

• Module Index

• Search Page