Classiq/classiq-library
May 2026 – Jun 2026
2 Months active
Languages Used
JSONPythonQASM
Technical Skills
Python programmingalgorithm optimizationdata managementquantum computingData ProcessingJupyter Notebook
Din Carmon
PROFILE
Din Carmon
Worked on the Classiq/classiq-library repository, focusing on quantum circuit synthesis and notebook workflow stability. Delivered hardware-aware synthesis improvements by optimizing parameter binding, enhancing compatibility with evolving QStruct outputs, and refining memory management in test workflows. Used Python, QASM, and Jupyter Notebook to align synthesis configurations with notebook constraints, improve resource utilization, and ensure reproducibility across environments. Addressed dependency management by updating requirements and pruning unnecessary packages, which stabilized notebook CI execution. Validated end-to-end production parity by reproducing statevector results with high fidelity, reducing maintenance overhead and supporting reliable quantum computing workflows for production and research users alike.
Overall Statistics
Feature vs Bugs
40%Features
Repository Contributions
9Total
Bugs
3
Commits
9
Features
2
Lines of code
14,425
Activity Months2
Your Network
87 peopleShared Repositories
54
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Work History
June 2026
7 Commits • 1 Features
Jun 1, 2026June 2026 — Delivered substantial business value through robust hardware-aware synthesis improvements, enhanced data-structure compatibility, and notebook stability in Classiq/classiq-library. Key synthesis-flow fixes increased reliability and performance on hardware mapping, and output handling was made backward-compatible with new monomorphized QStruct outputs. Notebook CI stability was improved by pinning and pruning dependencies. Overall, the work reduces maintenance churn, expands hardware support, and improves the fidelity and predictability of quantum circuit synthesis for production users.
7 Commits • 1 Features
Jun 1, 2026June 2026 — Delivered substantial business value through robust hardware-aware synthesis improvements, enhanced data-structure compatibility, and notebook stability in Classiq/classiq-library. Key synthesis-flow fixes increased reliability and performance on hardware mapping, and output handling was made backward-compatible with new monomorphized QStruct outputs. Notebook CI stability was improved by pinning and pruning dependencies. Overall, the work reduces maintenance churn, expands hardware support, and improves the fidelity and predictability of quantum circuit synthesis for production users.
June 2026
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for Classiq/classiq-library: Focused on aligning synthesis configuration with notebook constraints and improving memory hygiene in test workflows, delivering measurable improvements in reproducibility and resource utilization.
May 2026
2 Commits • 1 Features
May 1, 2026May 2026 monthly summary for Classiq/classiq-library: Focused on aligning synthesis configuration with notebook constraints and improving memory hygiene in test workflows, delivering measurable improvements in reproducibility and resource utilization.
Activity
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Quality Metrics
Correctness100.0%
Maintainability89.0%
Architecture86.6%
Performance89.0%
AI Usage75.4%
Skills & Technologies
Programming Languages
JSONPythonQASM
Technical Skills
Data ProcessingJupyter NotebookPythonPython programmingQASMQuantum ComputingSynthesis Optimizationalgorithm designalgorithm optimizationci/cddata analysisdata managementdependency managementpythonquantum computing
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