Changelog

Automatically track UCC compiler passes with MLflow.

How to execute jobs in Qubernetes projects withouth having to wait for completion.

How to specify the Python version for your workloads in Qubernetes projects.

Enable custom docker registries for storing Qubernetes job images

How to run workloads that consist of multiple files with Qubernetes.

How to set the default kubeconfig file for Qubernetes projects.

Launch Jupyter Lab with the correct environment variables set from the Qubernetes CLI.

View Matplotlib plots from Qubernetes job runs in the Jupyter notebook.

Introducing Qubernetes projects, a new way to organize your workspace, dependencies and automate the preparation of the containers needed to run your experiemnts on different different quantum simulators and hardware.

Qubernetes CLI now supports executing jobs on CPU and QPU targets.

Qubernetes CLI is available as a Python package and can be installed using pip.

Introducing Qubernetes Studio, a GUI application for starting and managing Qubernetes Jupyter clusters.

We have introduced the option to execute quantum routines using images from private registries. To be able to use images from private registries, you need to authenticate with the registry. This can be done by creating a personal access token (PAT) and using it to execute Python programs or Jupyter notebooks on the Qubernetes cluster.

We have introduced a new command q8sctl execute to run a Python program on Qubernetes. The command takes a Python script as an argument and executes it on the Qubernetes cluster. The script can be any Python program that you want to run on the cluster. The command will create a new job on the cluster and execute the script in a container.

We have introduced support for environment variables for the Jobs that you run on Qubernetes. Environment variables can be set in the .env.q8s file that should be placed in the root directory of the workspace.