Skip to content

Pulse and control

PyQLab

Maintained by BBN-Q

This is a python package for managing instruments and control parameters for superconducting qubit systems.

PythonApache-2.0
PyQLab illustration

Resource snapshot

Category

Pulse and control

Stars

25

Last pushed

Jun 6, 2017Updated 9y ago

Open issues

6

What it is

PyQLab is maintained by BBN-Q and sits in the Pulse and control lane of the open-source quantum map.

This is a python package for managing instruments and control parameters for superconducting qubit systems.

Last verified by Qtangl generator on May 27, 2026

Who it's for

Hardware-adjacent developers and researchers working closer to experiments, pulse schedules, or lab orchestration.

What you can build or learn

  • See how low-level control stacks differ from higher-level SDKs.
  • Understand where pulse sequencing and experiment orchestration fit in the stack.
  • Learn which projects matter when hardware control is part of the workflow.

License

Apache-2.0

SPDX identifier detected from the repository metadata or license files.

Repository README

Preview from the project README.

Rendered as Markdown inside a scrollable preview. Long READMEs stay contained; expand or open on GitHub for the full document.

~222 words · about 1 min readOpen on GitHub

PyQLab Instrument and Qubit Control Software

Build Status Coverage Status

This is a python package for managing instruments and control parameters for superconducting qubit systems. It complements the Qlab repository by providing simple GUIs for creating the JSON settings structures used by Qlab.

Setup instructions

The most straightforward way to get up and running is to use the Anaconda Python distribution. This includes nearly all the dependencies. The few remaining can be installed from the termminal or Anaconda Command Prompt on Windows. On Windows you may also need to ensure that either the already installed git is on the path or conda install git.

Python 2

conda install atom enaml future
pip install watchdog
pip install git+https://github.com/BBN-Q/QGL.git

Python 3

PyQLab depends on enaml/atom which have only recently become Python 3 compatible via a fork. You may wan to run PyQlab from its own environment to segregate its dependencies from your standard environment.

conda create --name pyqlab python=3.6 scipy networkx h5py bokeh
source activate pyqlab
conda install -c ecpy enaml watchdog
pip install git+https://github.com/BBN-Q/QGL.git

The PyQLab config file will be created the first time you run ExpSettingsGUI.py.

Dependencies

  • Python 2.7/3.5
  • Numpy/Scipy
  • Nucleic enaml/atom
  • h5py
  • future
  • watchdog
  • Bokeh 0.7
  • iPython 3.0 (only for Jupyter notebooks)
  • QGL
Read on GitHub

Activity

Latest release

—

Watchers

23

Python support

—

Learn digest

Get monthly updates when library entries change.

Monthly digest: new library entries, updated flagships, and one editorial pick.

Related resources

Keep exploring nearby tools.

m-labs

artiq

A leading-edge control system for quantum information experiments

PythonLGPL-3.0FlagshipArchive

515 stars · Updated 7mo ago

quantumlib

Cirq

Python framework for creating, editing, and running Noisy Intermediate-Scale Quantum (NISQ) circuits.

PythonApache-2.0Flagship

4,971 stars · Updated 3mo ago

XanaduAI

pennylane

PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry.

PythonApache-2.0FlagshipQtangl relevant

3,229 stars · Updated 3mo ago

BBN-Q

pyqgl2

An imperative Quantum Gate Language (QGL) embedded in python.

PythonApache-2.0

9 stars · Updated 4y ago

QISKit

qiskit

Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.

PythonApache-2.0FlagshipQtangl relevant

7,412 stars · Updated 3mo ago

Microsoft

QuantumKatas

Tutorials and programming exercises for learning Q# and quantum computing

Jupyter NotebookMITFlagshipArchive

4,862 stars · Updated 2y ago