Source-linked AI summary
Qudi: a modular python suite for experiment control and data processing
Jan M Binder, Alexander Stark, Nikolas Tomek, Jochen Scheuer, Florian Frank, Kay D Jahnke, Christoph Müller, Simon Schmitt, Mathias H Metsch, Thomas Unden, Tobias Gehring, Alexander Huck, Ulrik L Andersen, Lachlan J Rogers, Fedor Jelezko
TL;DR
Laboratories need flexible software to coordinate heterogeneous hardware and process measurements, but mature control software is scarce. Qudi addresses this gap with a modular Python suite using hardware abstraction, experiment logic, and interfaces, demonstrated in quantum-optics workflows. Its scope is broad, while current development still includes setup and automation limitations.
Problem
Laboratory experiments require coordinated control of diverse devices, yet mature and flexible laboratory control software is scarce.
Method
Qudi uses modular Python components, hardware interfaces, logic modules, and interfuses to coordinate devices and process measurements across experiment workflows.
Results
Qudi provides a generally applicable experiment-control infrastructure and an established quantum-optics toolset capable of reliable laboratory operation.
Takeaways & Limitations
The modular infrastructure supports reuse across small and medium-sized computer-controlled experiments beyond confocal microscopy and physics.
Takeaways & Limitations
Future work includes simplifying setup with a graphical configuration editor, enabling Python Package Index installation, and improving automation without programming.
Abstract
from arXiv · showhide
Qudi is a general, modular, multi-operating system suite written in Python 3 for controlling laboratory experiments. It provides a structured environment by separating functionality into hardware abstraction, experiment logic and user interface layers. The core feature set comprises a graphical user interface, live data visualization, distributed execution over networks, rapid prototyping via Jupyter notebooks, configuration management, and data recording. Currently, the included modules are focused on confocal microscopy, quantum optics and quantum information experiments, but an expansion into other fields is possible and encouraged. Qudi is available from https://github.com/Ulm-IQO/qudi and is freely useable under the GNU General Public Licence.
1. Motivation and significance
Qudi addresses the lack of mature, flexible software for coordinating diverse laboratory hardware and processing measurement data. It presents an open-source Python suite developed for complex experiments but designed for broader applicability.
- Laboratory experiments require software that coordinates multiple hardware devices and supports rapid data interpretation through processing and visualization.
- A paucity of mature and flexible laboratory control software motivates Qudi’s development.
- Qudi controls complex experiments while managing measurement-data acquisition and processing.
- Although developed for quantum optics laboratories, Qudi’s core framework is broadly applicable to coordinated multi-device experiments.
2. Software description
Qudi organizes experiment control through a manager and separated hardware, logic, and interface modules. Interfaces and interfuses abstract device differences and enable reuse, while graphical and notebook-based control support both interactive and scripted operation.
- Core infrastructure: Qudi’s manager loads modules from configuration settings and provides logging, error handling, dependency resolution, concurrent execution, and remote access.
- Three-layer architecture: The three-layer design separates hardware interaction, experiment logic, and user interfaces to improve reliability, flexibility, and implementation of new modules.
- Experiment logic: Logic modules synchronize experiments, exchange information between hardware modules, compute results, and handle measurement execution through data storage.
- Hardware abstraction via interfaces: Hardware modules translate logic commands into device-specific languages, allowing functionally similar but differently communicating devices to be interchanged.
- Hardware abstraction via interfaces: Dummy hardware can emulate devices or load recorded data, enabling experiment logic to be tested without physical hardware.
- Advanced abstraction via interfuses: Interfuses connect hardware or logic modules, modify interface behavior, and support tasks beyond the modules’ original designs.
- Advanced abstraction via interfuses: Replacing counter data with spectrometer measurements enables fluorescence imaging with arbitrary spectral filtering while improving maintainability and preventing code duplication.
- User interfaces: GUI modules provide experiment control and visualization, while logic can also be operated through the IPython console or a Jupyter notebook.
3. Impact and reuse potential
Qudi targets small and medium-sized computer-controlled experiments through modular, interface-based reuse. It already provides a substantial quantum-optics toolset independent of particular hardware and measurement schemes.
- Qudi’s modular design and interfaces simplify integrating new hardware and reusing existing modules in new experiments.
- Its core infrastructure is intended to extend beyond confocal microscopy and physics experiments generally.
- Existing quantum-optics modules control confocal microscopes, electromagnets, motorized stages, lasers, signal generators, and other devices.
- Implemented measurement protocols and data-analysis functions make Qudi ready for quantum-optics laboratories across different hardware and measurement schemes.
4. Illustrative example
Qudi demonstrates coordinated ODMR measurement by combining confocal positioning, fluorescence detection, microwave-frequency control, live visualization, and scripted multi-site acquisition. The example shows how its modules support an end-to-end laboratory workflow.
- ODMR measurement coordinates a scanning confocal microscope with a microwave source to study single color centers in diamond.
- Confocal positioning: Confocal scanning hardware moves the focal spot in three dimensions while photon counting produces fluorescence images for user-guided positioning.
- Confocal positioning: An optimizer uses close-range scans and Gaussian fits to locate the maximum-fluorescence position of a selected color center.
- ODMR acquisition: The ODMR logic sweeps microwave frequency while recording fluorescence, and its interface supports different microwave-source hardware.
- ODMR acquisition: Live displays show summed fluorescence across frequency sweeps and a matrix containing each individual sweep.
- Automation: Saved color-center positions and scripting enable automatic ODMR scans across multiple sites, with position optimization at each site.
5. Conclusions and future directions
Qudi provides generally applicable infrastructure for modular experiment design, with a developed quantum-optics tool set and modern user interface. Future work targets broader module coverage, easier setup, and more automation.
- Qudi’s modular infrastructure significantly reduces the effort required to construct new experiments.
- Its quantum-optics tool set is developed for reliable laboratory operation and includes a modern user interface.
- Future development will expand the science-module library and add a graphical configuration editor and Python Package Index installation.
- Enhanced automation is desired so users can rearrange existing functionality without programming.