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Quanlse (量脉) is a cloud-based platform for quantum control developed by the Institute for Quantum Computing at Baidu Research. Quanlse aims to bridge the gap between quantum software and hardware. It provides efficient and professional quantum control solutions via an open-source SDK strengthened by Quanlse Cloud Service.

Quanlse supports the pulse generation and scheduling of arbitrary single-qubit and two-qubit gates. With the help of toolkits in Quanlse, one can use Quanlse for modeling real superconducting quantum chips, simulating noisy quantum devices and dynamical evolution, visualizing error analysis, and characterizing and mitigating error. Single/two-qubit gates and general Mølmer-Sørensen gate realization on the trapped ion platform and relevant applications on the NMR platform are also available in Quanlse. For the practicality in experiments, Quanlse provides the toolkit for qubit and readout cavity calibration. Furthermore, Quanlse supports pulse-level control of quantum algorithms and advanced R&D (Research & Development) in the field of quantum control.

Quanlse v2.2

Attention: We have added some exciting features and further improved the original ones in Quanlse v2.2. We strongly recommend users to upgrade to Quanlse v2.2!

In this update, we provide Lab package to support the superconducting quantum computing experiment, as well as the interface for experiment parameter service and hardware access. At the same time, we improved the fundamental modules to support the flexible defining of control pulses and control channels. In trapped ion, we provide a robust quantum laser pulse control solution, and provide some fundamental modules for quantum laser, ion chip and ion-phonon evolution trajectory to reveal the noise influence on trapped ion system.


We strongly recommend using Anaconda for your R&D environment and upgrading the requirements to the latest versions for the best experience.

Install via pip

We recommend the following way of installing Quanlse with pip

pip install Quanlse


If you have already installed Quanlse, use the following code to update

pip install --upgrade Quanlse

Download and install via GitHub

You can also download all the files and install Quanlse locally

git clone http://github.com/baidu/Quanlse
cd Quanlse
pip install -e .

Run programs

Now, you can try to run a program to verify whether Quanlse has been installed successfully

cd Example
python 1-example-pi-pulse.py

Introduction and developments


To get started with Quanlse, users are recommended to go through the Overview firstly to acquire the whole picture of this platform. Quick Start could then be a good place to guide you on how to use Quanlse Cloud Service step by step and how to construct your first program using Quanlse. Next, users are encouraged to learn more functions and applications from the tutorials Quanlse provided. Finally, it would be great if users could solve their own problems using Quanlse. For complete and detailed documentation of the Quanlse API, please refer to our API documentation.


Quanlse provides detailed and comprehensive tutorials from fundamental to advanced topics. Each tutorial currently supports reading on our website. For interested developers, we recommend them to download Jupyter Notebooks and play with it. The tutorial list is as follows:


Users are encouraged to contact us through Github Issues or quanlse@baidu.com with general questions, bugs, and potential improvements. We hope to make Quanlse better together with the community!

Frequently Asked Questions

Q: How should I get started with Quanlse?

A: We recommend users go to our website and follow the roadmap.

Q: What should I do when I run out of my credit points?

A: Please contact us on Quantum Hub. First, you should log into Quantum Hub, then enter the "Feedback" page, choose "Get Credit Point", and input the necessary information. Submit your feedback and wait for a reply.

Q: How should I cite Quanlse in my research?

A: We encourage developers to use Quanlse to do research & development in the field of quantum control. Please cite us by including BibTeX file.


The changelog of this project can be found in CHANGELOG.md.

Copyright and License

Quanlse uses Apache-2.0 license.


[1] Quantum Computing - Wikipedia.

[2] Nielsen, Michael A., and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge: Cambridge UP, 2010. Print.

[3] Werschnik, J., and E. K. U. Gross. "Quantum optimal control theory." Journal of Physics B: Atomic, Molecular and Optical Physics 40.18 (2007): R175.

[4] Wendin, Göran. "Quantum information processing with superconducting circuits: a review." Reports on Progress in Physics 80.10 (2017): 106001.

[5] Krantz, Philip, et al. "A quantum engineer's guide to superconducting qubits." Applied Physics Reviews 6.2 (2019): 021318.


量脉(Quanlse)是由百度研究院量子计算研究所开发的基于云服务的量子控制平台。 expand collapse
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