Environments

The qBraid Lab Environment Manager is a streamlined quantum software package and virtual environment management system provided to qBraid end-users through a simple, intuitive graphical user interface. It offers curated development environments in Python, Julia, C++, and Q# over a range of quantum applications, is highly customizable, and facilitates direct connections to quantum hardware.

To expand the Environment Manager sidebar, click on Envs in the upper-right of the Lab console. My Environments are your currently installed environments. The qBraid Default Python environment is pre-installed for all users. For Standard/Pro users, a number of other environments are also available pre-installed. These include Microsoft QDK (Q#), the Intel® Quantum SDK (C++), Bloqade (Julia), cuQuantum (CUDA), and more.

Key Features

  • Pre-Packaged Environments:

    qBraid Lab provides a suite of 30+ pre-packaged virtual environments dedicated to popular quantum software packages. These include but are not limited to Qiskit, Cirq, Amazon Braket, Pennylane, PyQuil, TensorFlow Quantum, and more. Each environment incorporates not only the primary packages but also recommended add-ons and configurations. Environments are pre-configured to function out-of-the-box, eliminating the hassle of manual installations and potential dependency conflicts.

  • Comprehensive Environment Search:

    Locate and install the ideal environment for your project by searching by name, description, tag, or package. This intuitive search feature ensures you find an environment tailored to your use case.

  • Version Management:

    Stay updated without the hassle. Minor software updates are reflected in the pre-packaged environments, which users can reinstall to access. For significant or breaking updates, entirely new environments are made available, preserving the integrity of projects relying on earlier versions.

  • Customize and Share:

    Customize pre-packaged environments by installing or upgrading packages, or create your own custom environments that can be shared directly with other qBraid Lab users.

  • Detailed Dependency Overview:

    Each environment showcases a complete list of its packages, akin to a pip freeze. Users can browse and search through these packages pre- and post-installation, making it easy to keep track of the tools at their disposal. This package list is dynamically updated to mirror any user-driven changes.

  • Direct Hardware Connectivity:

    Environments marked with the quantum-jobs tag can be used to connect directly to a range of QPU devices from leading providers, including IonQ, Oxford Quantum Circuits, QuEra, Rigetti, and AWS on-demand simulators, with no additional access keys required.

  • Command-Line Interface:

    For those who prefer terminal-based interactions, qBraid Lab provides a command-line interface that mirrors the functionalities of the GUI-based Environment Manager. Users can activate environments, manage kernels, install new packages, enable quantum jobs, and much more directly from their terminal.

Install environment

1. In the Environment Manager sidebar, click Add to view the environments available to install.

2. Choose an environment, expand its panel, and click Install.

Install environment

3. Once the installation has started, the panel is moved to the My Environments tab. Click Browse Environments to return to the My Environments tab and view its progress.

Installing environment progress bar

4. When the installation is complete, the environment panel’s action button will switch from Installing… to Activate.

Create environment

Create custom environment

1. In the Environment Manager sidebar, click Add, then click Create Environment.

2. Give your custom environment a name, description, add tags, upload a logo, and specify any packages to install using a requirements.txt format.

After clicking Create, a new environment panel is created for your custom environment. You can view the environment’s install progress by returning to My Environments.

Installing custom environment progress

3. Once the environment has been created and any package installations have finished, the environment panel’s action button will switch from Installing… to Activate, and the installed packages number will be updated.

4. Click on More to verify/view the environment’s list of installed packages. You can use the search bar or scroll through the Python package (pip) list to find the exact versions of all packages and package dependecies that were installed. From the More pop-out, you can also install additional packages, remove packages, add/delete tags, and edit the environment’s description.

Share environment

Share environment

In qBraid Lab, you can share your custom environments with other users. This will make your environment visible under their Add environment list. They can then install it, and run code using an exact copy of your environment. This includes support for quantum jobs and any additional configurations.

Although modifications to pre-packaged environments can’t be directly shared, you can export a requirements.txt from any environment to use in creating a new custom environment “copy”, which can then be shared with other users.

To share a custom environment, first click the More button in the drop-down to open the environment editor. Then, under ‘Share Environment’ , enter the qBraid user email, and click Share.

When you share an environment on qBraid, you’re creating a snapshot of your Python virtual environment with its specific configurations and installed packages. This snapshot is uploaded to the qBraid cloud, creating a static version accessible to other users. This shared version remains unchanged, even if you make updates to your local environment. To reflect any changes, you’d need to re-share and overwrite the existing version in the cloud.

Overwriting a globally shared environment doesn’t affect versions that other users have already downloaded. Instead, an indicator notifies them of the availability of an updated version. Currently, users must manually check for this indicator. Also, before installing an updated version, users must uninstall their current one as maintaining multiple versions of a shared environment isn’t supported. Sharing or overwriting globally doesn’t impact your local environment.

Uninstall / cancel install environment

To uninstall an environment or cancel the installation of an environment, click on More, and then Uninstall or Cancel Installation.

Cancel installation or uninstall environment

Install new package

There are three ways to install a new package into an environment on qBraid:

1. Add a package button

Open the Environment Manager (ENVS) sidebar, and find the environment into which you want to install the package. Expand the environment panel using the carrot drop-down, and then click More.

At the bottom of the Python packages list, click Add a package. Type in the exact name of the package you wish to install into the “Add package…” search bar, and then hit Enter (or click the search icon on the right). This will do a direct search through PyPI, and return the latest version available to install. To accept, click on the packge, and then click Add.

Add package to environment

While installing, the environment action button will indicate Installing…. Once complete, Lab will notify with a pop-up.

2. IPython magic command

From inside a notebook, run your pip command using the “magic” % operator to install the desired package(s) within the current kernel:

%pip install [pkgs]

After installing any new packages directly from a notebook, you must restart the kernel to see the changes take effect.

See also

3. Directly from terminal

From Launcher, under “Other”, click on Terminal. By default, your qBraid terminal opens using Python (and pip) from /opt/conda/bin. Packages that are installed directly at this top-level will not persist between sessions. Instead, use the qBraid CLI to list your environments,

$ qbraid envs list
# installed environments:
#
qsharp                         /opt/.qbraid/environments/qsharp_b54crn
default                  jobs  /opt/.qbraid/environments/qbraid_000000
qbraid_sdk               jobs  /home/jovyan/.qbraid/environments/qbraid_sdk_9j9sjy
qiskit                         /home/jovyan/.qbraid/environments/qiskit_9y9siy

activate an environment, and perform your pip installs from there. For example, installing pytket into the qiskit environment:

$ qbraid envs activate qiskit
$ python -m pip install pytket
$ deactivate

Activate environment (kernel)

Clicking Activate creates a new ipykernel, see Add/remove kernels for more.

Activate environment kernel

Command-line usage

qBraid environments are simply Python virtual environments in disguise. There are two directories where qBraid environment can be found. One at the system level, and one at the local / user level:

$ echo $QBRAID_USR_ENVS
/home/jovyan/.qbraid/environments
$ echo $QBRAID_SYS_ENVS
/opt/.qbraid/environments

The environments at the the system level are reset each time you launch Lab, so any changes made to these environments will not persist between sessions. The user level environments are yours to modify: any and all changes you make to these environments will still be there the next time to launch Lab. The qBraid CLI provides convenient short-cuts to activate and install pacakages into your qBraid environments, however all of the traditional commands to activate and manipulate Python venvs still apply. For example, you can activate an environment using the source command:

$ source $QBRAID_USR_ENVS/<slug>/pyenv/bin/activate

If you’re working in terminal, you don’t specifically need to activate a qBraid environment, as you can just specify the full path to that environment’s Python interpreter when invoking Python, e.g. /<path-to-venv>/bin/python. The same is true for pip commands. So if you are attempting to install new packages into a qBraid environment from terminal or inside a notebook, make sure to use the full pip path, or use one of the three approaches detailed above. Simply running pip install ... will default to /opt/conda/bin/pip, which will cause dependency conflicts, and the installed packages will be overridden the next time you launch lab.