Classiq/classiq-library
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
Jupyter NotebookPythondata analysisimage processingquantum algorithmsquantum computing
Yuval Cohen
PROFILE
Yuval Cohen
Yuval Cohen enhanced quantum image processing workflows in the Classiq/classiq-library repository, focusing on edge detection performance and accuracy. He refactored the core edge detection logic using Python and Jupyter Notebook, improving both throughput and reliability for image analysis tasks. Yuval also reorganized notebook execution order to ensure correct processing flow, which streamlined validation and reduced maintenance overhead. Additionally, he removed obsolete Hamming weight tests and related artifacts, reflecting a shift in project priorities. His work demonstrated depth in quantum algorithms and data analysis, resulting in a more robust, maintainable codebase ready for broader deployment in quantum computing applications.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
2,485
Activity Months1
Your Network
80 people
Work History
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Classiq/classiq-library: Focused on stabilizing core image processing workflows and improving edge detection performance and accuracy. Delivered a refactor of edge detection logic, adjusted notebook execution order to ensure correct processing, and cleaned up test/workspace artifacts to reduce maintenance and noise in the validation suite. These improvements enhance throughput, reliability, and readiness for broader deployment while maintaining high-quality image analysis results.
3 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for Classiq/classiq-library: Focused on stabilizing core image processing workflows and improving edge detection performance and accuracy. Delivered a refactor of edge detection logic, adjusted notebook execution order to ensure correct processing, and cleaned up test/workspace artifacts to reduce maintenance and noise in the validation suite. These improvements enhance throughput, reliability, and readiness for broader deployment while maintaining high-quality image analysis results.
December 2025
Activity
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Quality Metrics
Correctness66.6%
Maintainability73.4%
Architecture66.6%
Performance73.4%
AI Usage26.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Jupyter NotebookPythondata analysisimage processingquantum algorithmsquantum computingquantum programmingtesting
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