Software Installation#

On this page, we are going to look at the installation steps for the various tools needed to operate Qblox hardware. In addition, we will explain how to connect a Qblox cluster to your host PC.

Qblox Clusters work out of the box with the qblox-instruments Python driver. In this guide, we will walk you through setting up a virtual Python environment, installing the qblox-instruments driver package, and adding supporting tools such as Jupyter for interactive development.

In addition, we introduce Qblox Scheduler. It enables hybrid gate–pulse programming with precise timing control, while taking care of compilation and instrument communication in the background—allowing you to focus on designing and running quantum experiments.

Qblox Instruments#

In this section, we will explain how to install the qblox-instruments package and set it up for use with Qblox hardware.

Note

Qblox Instruments and Qblox Scheduler require Python 3.10 or newer.

Tip

Tip for new Python users

For users new to Python, we recommend using Anaconda to set up your programming environment.

Please follow the installation instructions provided by Anaconda, and open up an Anaconda prompt when the installation is finished. We will start by creating a new environment, which we will use to install packages independently from other conda or Python environments. We can specify the name and the Python version of the environment. Type the following in Anaconda prompt (we chose my-env-name as the name for the environment which could be replaced with another name of your choice):

$ conda create -n my-env-name python=<your-python-version>

Then activate the environment by typing conda activate my-env-name. You are now ready to proceed with the next steps. For a quick overview of useful conda commands, we recommend going through the conda cheat sheet.

System dependencies#

Qblox Instruments has one important system dependency, namely the HDF5 library. If this library is not installed on your system yet, please follow the installation instructions on the HDF5 website.

New users of HDF5 should download the latest version for their operating system under the section Pre-built Binary Distributions.

Note

(Windows only) If, after installation of the HDF5 library, you run into errors regarding Visual Studio C++, please install Microsoft C++ Build Tools as well.

Installation#

The qblox-instruments driver package can be installed through pip, by executing the following command (make sure you have activated the correct environment using conda activate my-env-name):

$ pip install qblox-instruments

This will install the most recent version of the driver package. Please make sure that the driver version you install is compatible with your Cluster firmware, verify via the driver changelog on PyPI. For information on the firmware and driver updating procedure, see section Updating.

To install a specific version of the driver package, execute the following command:

$ pip install qblox-instruments==<version>

Tip

You can query your installed version by executing $ pip show qblox-instruments.

With qblox-instruments, two command-line tools will have been installed in your environment: Qblox Configuration Manager and Qblox Plug & Play. We will be using these tools in the next step of connecting to your device(s): Cluster Network and Host Setup. In addition, you might have noticed the QCoDeS dependency, which is the experiment and data acquisition framework used to control the Qblox hardware.

JupyterLab#

Our tutorials are run on the interactive iPython platform called Jupyter. We recommend installing jupyterLab which provides a user-friendly interface for maintaining these notebooks. Following are the steps to install it:

As always, start by activating the conda environment if you haven’t already:

  $ # Replace my-env-name with the name of your environment
  $ conda activate my-env-name

Install jupyterLab from conda

  $ conda install -c conda-forge jupyterLab

Once installed, run using
```
  $ jupyterLab
```

Qblox Scheduler#

To get the most out of your Qblox electronics, we advise installing qblox-scheduler, our high level measurement control package. Qblox scheduler provides a framework for writing quantum circuits featuring a hybrid gate-pulse control model with explicit timing control. It abstracts away the complexities of compilation and instrument control, allowing users to focus on their experiments. For more information please visit the User guide.

To install Qblox Scheduler via pip, please run:

$ pip install --pre qblox-scheduler

where the --pre flag is used to install the latest beta version of qblox-scheduler.

when new versions are released, you can upgrade to the latest beta version by running:

$ pip install --upgrade --pre qblox-scheduler

or to a specific version with:

$ pip install qblox-scheduler==<version-no>

This will also install and update the compatible driver (qblox-instruments) automatically. You can verify the correct versions and dependencies via the driver changelog on PyPI.