pasqal-io/Pulser
Mar 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentData ValidationJSONSerializationTestingNoise Modeling
Anton Quelle
PROFILE
Anton Quelle
Worked on the pasqal-io/Pulser repository to enhance results storage, serialization, and simulation fidelity for quantum computing workflows. Developed a centralized storage core for experiment results, standardizing data persistence by UUID, tag, time, and value, and extended JSON serialization to support NumPy arrays and PyTorch tensors with schema validation for data integrity. Improved cross-framework compatibility, enabling seamless integration with machine learning pipelines. Addressed simulation accuracy by fixing energy moment calculations for density matrices and introducing detuning fluctuations as a new noise type. Leveraged Python for backend development, data validation, and numerical simulation, demonstrating technical depth in quantum computing and noise modeling.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
413
Activity Months2
Your Network
35 peopleWork 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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