arbok-driver 🐍⚡️

Documentation Status PyPI License Coverage Python

A dynamically generated QCoDeS instrument for your FPGA based measurements using the Quantum Machines OPX+/OPX1000.

Installation

From pypi install using pip in your environment:

pip install arbok-driver

Even better if you are using uv, a uv.lock file is included!

Features 🛠️

  • Abstraction: In contrast to traditional static QCoDeS instruments with a fixed set of parameters determined at driver design time, arbok is dynamically generated for the FPGA program itself each time a measurement is run. FPGA progam parameters such as wait times, voltages, and frequencies etc. are automatically exposed through the QCoDeS instrument, without the user needing to interact with or even see the underlying FPGA code.

  • Modularity: Measurements are generated from logical blocks of instructions called SubSequence and ReadSequence whose order can be arranged freely. Those sequences can be nested arbitrarily deep to reach the desired complexity without repeating yourself (imagine a nested dict).

  • Scalability: Arbok strictly separates the qualitative design of FPGA instructions in SubSequence and ReadSequence and their quantitative configuration (through python dicts), ensuring reproducibility. Sequences are written in a fully parameterized way, giving user access to every aspect of the measurement. This approach allows us to grow our quantum chips without re-writing any FPGA sequences by just scaling the configuration we are using, while still exposing every single sub-parameter of the device.

  • Compatibility: At the end of the day, the arbok-driver is still a QCoDeS instrument, giving you all the features you know from running measurements on regular hardware. It slots right into your existing stack and allows you to sweep hardware parameters as well as FPGA instructions.

  • Auto-tuning: All parameters can be defined as undetermined variables and updated by the user in real time without needing to re-compile the FPGA program for each set of parameters. This is particularly interesting for adaptive calibrations, machine learning or qubit benchmarking.

  • Asynchronicity: Support for asynchronously running sequences like qubit state heralding or live qubit feedback (e.g Larmor, Rabi)

Tutorials🎓

The tutorial series introduces the core concepts of the arbok framework, guiding you from running basic measurements to building modular, scalable, and automated experiments.

Set up your environment, initialise your device, and run a first measurement using existing sequences.

Learn how FPGA program parameters (e.g. voltages, timings, frequencies) are exposed through the QCoDeS interface and how to configure them dynamically.

Understand how to use and compose ReadSequences to extract measurement results in a structured and reusable way.

Build higher-level experiment abstractions to repeatedly run and organise complex measurement routines.

Implement automated tuning and calibration routines using the GenericTuningInterface, enabling adaptive and scalable workflows without recompilation of FPGA instructions

Learn about dynamic FPGA program reloading and real-time parameter updates for fast iteration and adaptive measurements

Implement an asynchronous heralded quantum state initialization via a real time repeat-until-success loop.

Together, these tutorials demonstrate how arbok enables:

  • Modular composition of measurements from reusable building blocks
  • Full parameter control without modifying FPGA/qua code
  • Seamless scaling from small devices to larger quantum systems
  • Fast iteration through dynamic reloading with Ekans

Developer guides

Learn how to write your own SubSequence from scratch: defining a ParameterClass, writing qua_sequence, scaling to multiple gates with ParameterMap, using lifecycle hooks, and composing sequences by nesting.

Learn how to write AbstractReadout subclasses and compose them into a ReadSequence: producing and consuming gettables, adding settable parameters, declaring temporary QUA variables, and writing the configuration.

Stripping jupyter notebooks 📒

We all love running measurements and analysis scripts from notebooks but keeping them up to date with version control can be a pain. Differeing outputs, timestamps and massive binary data can make your diffs massive. We include git hooks that automatically clear the outputs of your notebook once you commit it. The arbok_driver is configured declaratively so dont worry you will reach the same state again once you re-run your notebook.

Install the git hook so that your notebooks are stripped before committing.

Microsoft :

.\tools\git.hooks\setupMicrosoft.ps1

Linux :

./tools/git.hooks/setupLinux.sh

Use of AI

Large language models (LLMs) were used to help generate the documentation of this project, including the tutorials, developer guides, and examples. The core logic and code of the arbok-driver were written without the use of AI.

Project links

Key dates

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  • Released:
Newer release available (2.7.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
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Credits

Author: Andreas Nickl

Requires

Python >=3.12

Provides Extra

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