pasqal-io/Pulser
Mar 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentData ValidationJSONSerializationTestingNoise Modeling
Anton Quelle
PROFILE
Anton Quelle
Anton Quelle enhanced the pasqal-io/Pulser repository by centralizing results storage and extending serialization to support machine learning workflows. He introduced a private storage method that standardizes how results are persisted by UUID, tag, time, and value, improving maintainability and data integrity. Anton implemented JSON serialization and schema validation for results, enabling compatibility with NumPy arrays and PyTorch tensors. He also fixed energy moment calculations for density matrices and expanded the simulation’s noise modeling to include detuning fluctuations. Working primarily in Python, Anton demonstrated depth in backend development, data validation, and quantum simulation, delivering robust, maintainable improvements to the codebase.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
413
Activity Months2
Your Network
29 people
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered critical correctness fixes and enhanced noise modeling in Pulser, improving simulation fidelity and reliability for quantum experiments and planning.
2 Commits • 1 Features
Jun 1, 2025June 2025: Delivered critical correctness fixes and enhanced noise modeling in Pulser, improving simulation fidelity and reliability for quantum experiments and planning.
June 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for pasqal-io/Pulser focusing on results storage and serialization enhancements that improve data integrity, cross-framework compatibility, and maintainability. Highlights include centralizing result persistence and extending serialization to support ML-friendly data types.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 performance summary for pasqal-io/Pulser focusing on results storage and serialization enhancements that improve data integrity, cross-framework compatibility, and maintainability. Highlights include centralizing result persistence and extending serialization to support ML-friendly data types.
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture85.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentData ValidationJSONNoise ModelingNumerical SimulationQuantum ComputingSerializationSimulationTesting
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