arbok-driver 2.4.0
QCoDeS compatible driver for the OPX+ from Quantum Machines
License: BSD-3-Clause
Requires Python: >=3.12

# arbok_driver 🐍⚡️
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A dynamically generated [QCoDeS](https://github.com/microsoft/Qcodes) instrument for your FPGA based measurements using the Quantum Machines [OPX+](https://www.quantum-machines.co/products/opx/)/[OPX1000](https://www.quantum-machines.co/products/opx1000/).

## Installation 📲
[From pypi](https://pypi.org/project/arbok-driver/) install using pip in your environment:
```bash
pip install arbok-inspector
```
Even better if you are using uv, a uv.lock file is included!

## Features 🛠️
- <u>**Abstraction:**</u> 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.

- <u>**Modularity:**</u> 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).
  
- <u>**Scalability:**</u> 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.

- <u>**Compatibility:**</u> 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.

- <u>**Auto-tuning:**</u> 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. 

- <u>**Asynchronicity:**</u> Support for asynchronously running sequences like qubit state heralding or live qubit feedback (e.g Larmor, Rabi)

## Tutorials 🎓

Running measurements from existing sub-modules and readouts is very easy and is covered in the first 2 tutorials.
Tutorial 3 covers the manual writing of an experiment to easily run a certain type of measurement over and over again.
Manual writing of a `ReadoutSequence` and an `AbstractReadout` will be the subject of tutorial 5.
Finally we cover the `GenericTuningInterface` to implement a simple auto-tuning routine.

[Tutorial 0: Getting started](docs/0_Measurement_example.ipynb)

[Tutorial 1:](docs/1_parameterizing_sequences.ipynb)

[Tutorial 2:](docs/2_Readout_sequences.ipynb)

## 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
```