Classiq/classiq-library
Dec 2025 – Dec 2025
1 Month active
Languages Used
JSONPython
Technical Skills
PythonPython programmingalgorithm designdata analysisfinancial modelingoptimization
yigali-classiq
PROFILE
Yigali-classiq
Yigal developed two core features for the Classiq/classiq-library, focusing on financial risk analytics and quantum optimization. He implemented Value at Risk calculations using iterative quantum amplitude estimation, integrating a comprehensive test suite to ensure model accuracy and support VaR verification. Additionally, he enhanced the QAOA-in-QAOA MaxCut framework by introducing hybrid classical-quantum solver capabilities, improving test coverage, and reorganizing files for clarity. His work leveraged Python, algorithm design, and quantum computing, resulting in a scalable, maintainable codebase. These contributions deepened the library’s analytical capabilities and streamlined validation, supporting faster experimentation and more robust decision-making in quantum-assisted financial modeling.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
2
Lines of code
1,472
Activity Months1
Your Network
80 people
Work History
December 2025
9 Commits • 2 Features
Dec 1, 2025December 2025 delivered two business-critical features in Classiq/library with a focus on risk analytics and quantum-assisted optimization. VaR calculations were implemented using iterative quantum amplitude estimation, accompanied by a test suite for VaR models and quantum programs, plus test adjustments to support VaR verification. The QAOA-in-QAOA MaxCut framework was enhanced with hybrid classical-quantum solver capabilities, including test improvements and file renaming for clarity. No major bugs were reported; minor issues were addressed within the test pipelines. Overall, the work increases analytical capability for risk assessment and provides a scalable, maintainable quantum-classical optimization framework, enabling faster validation, experimentation, and decision-making. Technologies demonstrated include iterative quantum amplitude estimation, QAOA-in-QAOA, hybrid quantum-classical optimization, test automation, and code quality/organization improvements.
9 Commits • 2 Features
Dec 1, 2025December 2025 delivered two business-critical features in Classiq/library with a focus on risk analytics and quantum-assisted optimization. VaR calculations were implemented using iterative quantum amplitude estimation, accompanied by a test suite for VaR models and quantum programs, plus test adjustments to support VaR verification. The QAOA-in-QAOA MaxCut framework was enhanced with hybrid classical-quantum solver capabilities, including test improvements and file renaming for clarity. No major bugs were reported; minor issues were addressed within the test pipelines. Overall, the work increases analytical capability for risk assessment and provides a scalable, maintainable quantum-classical optimization framework, enabling faster validation, experimentation, and decision-making. Technologies demonstrated include iterative quantum amplitude estimation, QAOA-in-QAOA, hybrid quantum-classical optimization, test automation, and code quality/organization improvements.
December 2025
Activity
Loading activity data...
Quality Metrics
Correctness86.6%
Maintainability84.4%
Architecture88.8%
Performance82.2%
AI Usage28.8%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
PythonPython programmingalgorithm designdata analysisfinancial modelingoptimizationoptimization algorithmsquantum computingtesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline