Haiqu is a middleware platform that bridges the gap between quantum hardware and practical applications. Haiqu SDK gives you programmatic access to the full Haiqu cloud stack: error mitigation, circuit compression, hardware integration, and experiment tracking - so you can run quantum circuits that actually work on today's noisy hardware.

📖 Full documentation: docs.haiqu.ai

• Error Shield: automatic noise reduction preserves circuit fidelity without manual calibration
• Circuit Compression: run circuits that would otherwise exceed hardware qubit and gate limits
• Data Loading: linear-depth quantum data loading at practical scale
• Hardware Integration: connect to major quantum processors and cloud platforms through a single API
• Experiment Tracking: monitor job history, circuit measurements, and results across runs

For example, using Haiqu SDK for a utility-scale Floquet dynamics simulation of the kicked Ising model on 103 qubits reduces quantum processor runtime from ~3.1 hours to ~1 minute, and lowers the estimated quantum cloud bill from ~$17,856 to ~$34, while preserving accuracy close to the ideal result.

See the kicked_ising.ipynb notebook for the full experiment, as well as other SDK Application notebooks in the sdk-examples repository for more cases of practical advantage.

Want to see this in action? Book a demo to get access to this example and more.

Haiqu's technology is backed by peer-reviewed research:

• Matrix Product States for shallow quantum circuit synthesis
• Hamiltonian simulation via sub-block partitioning and state reconstruction
• Rivet transpiler: caching and reuse for iterative quantum workloads
• Fluid dynamics simulation without increasing circuit complexity
• Large Hamiltonian evolution across multiple quantum processors

See also the Haiqu Blog for more exciting research from us.

This repository contains the source code for the Haiqu SDK Python package. For users, It is recommended to install it from PyPI via pip install haiqu-sdk rather than to build from source. Refer to Haiqu SDK Docs / Local Installation guide for more details.

Prerequisites

• Python 3.10+ or Conda

Creating a virtual environment is optional, but highly recommended to avoid system-wide install and update of package dependencies.

Venv (available by default with Python)

python -m venv haiqu-env
source haiqu-env/bin/activate

Note: on Windows systems, instead of the shell command on the last line above, run .\haiqu-env\Scripts\activate.bat to activate the environment.

Conda

conda create -y --name haiqu-sdk python=3.13
conda activate haiqu-sdk

Virtualenv

virtualenv haiqu-env --python ">=3.10"
source haiqu-env/bin/activate

The latest version is available from PyPI

pip install haiqu-sdk

To use the Haiqu SDK you need an API key.

Request access here → haiqu.ai

After sign-up, your API key and access instructions will be delivered by email.

Once you have a key, authenticate in your code:

from haiqu.sdk import haiqu
  
# Login with your API key
haiqu.login(api_access_key="ENTER_YOUR_API_KEY_HERE")  # or set the HAIQU_API_KEY env variable
  
# Initialize your first experiment
haiqu.init("My First Quantum Experiment")

On success you will see:

Success: Welcome to the Quantum World, you@example.com!

You're all set. Refer to the Getting Started section to learn what you can do with Haiqu SDK.

The fastest way to get started is through the SDK tutorial notebooks in the sdk-examples repository. They act as a tutorial for basic Haiqu SDK feature with ready-to-run notebooks. Clone it and open the notebooks in your environment to get started:

git clone https://github.com/haiqu-ai/sdk-examples.git
cd sdk-examples

Also see the Core Features section of the Haiqu SDK docs for guides, as well as the full Haiqu SDK Reference.

Haiqu exposes MCP (Model Context Protocol) servers so you can use AI assistants like Claude or Cursor to execute circuits, query results, and browse documentation directly from your editor.

See MCP.md for configuration instructions for VS Code + Claude Code and Cursor.

• Documentation: docs.haiqu.ai
• Email: info@haiqu.ai
• Issues and feature requests: open an issue in this repository or visit feedback.haiqu.ai