qilimanjaro-tech/qililab
Oct 2024 – Jan 2026
10 Months active
Languages Used
PythonRSTMarkdownShellQASMYAMLJavaScript
Technical Skills
Class Hierarchy SimplificationCode OrganizationConfiguration RefactoringLibrary StructureModule RemovalRefactoring
Vyron Vasileiadis
PROFILE
Vyron Vasileiadis
Fedon Man worked extensively on the qilimanjaro-tech/qililab repository, delivering architectural improvements and robust features for quantum experiment workflows. He overhauled the quantum circuit compilation stack, introducing a native transpiler and compiler while removing legacy dependencies, and unified the IQ waveform hierarchy for better serialization and hardware mapping. Fedon implemented flexible I/Q offset handling in QProgram, streamlined configuration management with Pydantic, and modularized optional integrations for leaner installs. His work leveraged Python, YAML, and Pydantic, emphasizing code refactoring, dependency management, and test-driven development. These contributions improved maintainability, configurability, and reliability across quantum software and experiment execution pipelines.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
22Total
Bugs
5
Commits
22
Features
15
Lines of code
70,650
Activity Months10
Your Network
4 peopleShared Repositories
4
Work History
January 2026
2 Commits • 2 Features
Jan 1, 2026In January 2026, delivered a major architectural overhaul of the quantum circuit compilation stack for qililab, plus a flexible I/Q offset feature in QProgram. The overhaul removes legacy dependencies (Qibo) and introduces a native CircuitTranspiler and CircuitToQProgramCompiler, reorganizing the digital/pulse layers and standardizing IQ waveform handling. The IQ waveform hierarchy was unified under a new IQWaveform base class, with IQPair adapted and DRAG replaced by IQDrag; the Drag gate was renamed to Rmw. A set of transpilation passes (CancelIdentityPairsPass, CircuitToCanonicalBasisPass, FuseSingleQubitGatesPass, CustomLayoutPass, SabreLayoutPass, SabreSwapPass, CanonicalBasisToNativeSetPass, AddPhasesToDragsFromRZAndCZPass) now drive circuit decomposition and optimization. In parallel, Flexible I/Q offset types in QProgram were added to allow mixing constants and variables for I and Q offsets, enabling more flexible experimental configurations. This work was delivered with a breaking changes plan and enables downstream portability, serialization robustness, and better hardware mapping.
2 Commits • 2 Features
Jan 1, 2026In January 2026, delivered a major architectural overhaul of the quantum circuit compilation stack for qililab, plus a flexible I/Q offset feature in QProgram. The overhaul removes legacy dependencies (Qibo) and introduces a native CircuitTranspiler and CircuitToQProgramCompiler, reorganizing the digital/pulse layers and standardizing IQ waveform handling. The IQ waveform hierarchy was unified under a new IQWaveform base class, with IQPair adapted and DRAG replaced by IQDrag; the Drag gate was renamed to Rmw. A set of transpilation passes (CancelIdentityPairsPass, CircuitToCanonicalBasisPass, FuseSingleQubitGatesPass, CustomLayoutPass, SabreLayoutPass, SabreSwapPass, CanonicalBasisToNativeSetPass, AddPhasesToDragsFromRZAndCZPass) now drive circuit decomposition and optimization. In parallel, Flexible I/Q offset types in QProgram were added to allow mixing constants and variables for I and Q offsets, enabling more flexible experimental configurations. This work was delivered with a breaking changes plan and enables downstream portability, serialization robustness, and better hardware mapping.
January 2026
November 2025
4 Commits • 3 Features
Nov 1, 2025Concise monthly summary for 2025-11 focusing on key accomplishments across the qilimanjaro-tech/qililab repository.
November 2025
4 Commits • 3 Features
Nov 1, 2025Concise monthly summary for 2025-11 focusing on key accomplishments across the qilimanjaro-tech/qililab repository.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 highlights feature delivery and modularization for Quantum Machines (QM) integration. Implemented an optional import mechanism to switch between full concrete implementations and lightweight stubs based on environment and installed dependencies. Reorganized all QM-related components under the extra/quantum-machines module hierarchy for better modularity and maintainability. QM integration moved to project.optional-dependencies to reflect its optional nature, enabling lean installs by default and opt-in usage when needed. The change is documented with usage examples and aligns with our strategy to decouple optional integrations.
1 Commits • 1 Features
Oct 1, 2025October 2025 highlights feature delivery and modularization for Quantum Machines (QM) integration. Implemented an optional import mechanism to switch between full concrete implementations and lightweight stubs based on environment and installed dependencies. Reorganized all QM-related components under the extra/quantum-machines module hierarchy for better modularity and maintainability. QM integration moved to project.optional-dependencies to reflect its optional nature, enabling lean installs by default and opt-in usage when needed. The change is documented with usage examples and aligns with our strategy to decouple optional integrations.
October 2025
September 2025
4 Commits • 3 Features
Sep 1, 2025September 2025 highlights for qililab (qilimanjaro-tech/qililab). Delivered critical feature enhancements, stability improvements, and API/serialization refinements that directly boost researcher productivity, experiment reliability, and compute integration with Slurm-based workflows. Focused on business value through safer notebook execution, easier access to core APIs, and streamlined data handling, enabling faster iteration and reproducibility across projects.
September 2025
4 Commits • 3 Features
Sep 1, 2025September 2025 highlights for qililab (qilimanjaro-tech/qililab). Delivered critical feature enhancements, stability improvements, and API/serialization refinements that directly boost researcher productivity, experiment reliability, and compute integration with Slurm-based workflows. Focused on business value through safer notebook execution, easier access to core APIs, and streamlined data handling, enabling faster iteration and reproducibility across projects.
April 2025
3 Commits • 2 Features
Apr 1, 2025Month: 2025-04. Concise monthly summary for qilimanjaro-tech/qililab highlighting key feature deliveries, major bug fixes, and overall impact with technologies demonstrated. Key achievements and outcomes: - Unified Package Management and Build/Publish Migration to uv: Migrated packaging, build, and publishing to Astral's uv, centralizing dependencies in pyproject.toml and overhauling workflows to improve logging, automation, and consistency across the pipeline. Committed as [QHC-945] Update package management to `uv` (#923). Commit: eca50d94191bb5caf8dd4adcca3d5149972082c9. - CI: Parallel Test Execution with pytest-xdist: Enabled parallel test execution in GitHub Actions by integrating pytest-xdist, updating workflows, and adjusting local execution instructions to dramatically reduce test suite time. Committed as [QHC-949] Parallelize Tests (#925). Commit: 77937f9425e705ca752e9133bf981ac2e3adb867. - ExperimentExecutor Loop State Bug Fix and Tests: Refactored ExperimentExecutor to use instance variables for loop_indices, strengthening correctness and ensuring proper management of loop state in nested and parallel structures; added tests for validation. Committed as [QHC-950] Fix ExperimentResults Data (#932). Commit: eb36d6e36540ae23f93e985e5f9190fdd6595816. Impact: - Accelerated CI feedback with significantly reduced test durations and more reliable test execution, enabling faster iteration. - Improved build reliability and reproducibility by centralizing dependencies and modernizing the packaging/publishing pipeline. - Increased correctness for complex loop state scenarios in experiments, reducing risk of flaky behavior in parallel workflows. Technologies/skills demonstrated: - Python packaging and dependency management (pyproject.toml, uv integration) - CI/CD optimization (GitHub Actions, pytest-xdist) - Robust state management in Python (instance variables for loop state) - Test-driven improvements with added tests and coverage - Observability and logging enhancements across build and test pipelines Business value: - Shorter release cycles due to faster CI and more reliable builds; improved developer productivity through streamlined workflows and clearer logging; reduced risk in parallel/exhaustive test scenarios and experimental code paths.
3 Commits • 2 Features
Apr 1, 2025Month: 2025-04. Concise monthly summary for qilimanjaro-tech/qililab highlighting key feature deliveries, major bug fixes, and overall impact with technologies demonstrated. Key achievements and outcomes: - Unified Package Management and Build/Publish Migration to uv: Migrated packaging, build, and publishing to Astral's uv, centralizing dependencies in pyproject.toml and overhauling workflows to improve logging, automation, and consistency across the pipeline. Committed as [QHC-945] Update package management to `uv` (#923). Commit: eca50d94191bb5caf8dd4adcca3d5149972082c9. - CI: Parallel Test Execution with pytest-xdist: Enabled parallel test execution in GitHub Actions by integrating pytest-xdist, updating workflows, and adjusting local execution instructions to dramatically reduce test suite time. Committed as [QHC-949] Parallelize Tests (#925). Commit: 77937f9425e705ca752e9133bf981ac2e3adb867. - ExperimentExecutor Loop State Bug Fix and Tests: Refactored ExperimentExecutor to use instance variables for loop_indices, strengthening correctness and ensuring proper management of loop state in nested and parallel structures; added tests for validation. Committed as [QHC-950] Fix ExperimentResults Data (#932). Commit: eb36d6e36540ae23f93e985e5f9190fdd6595816. Impact: - Accelerated CI feedback with significantly reduced test durations and more reliable test execution, enabling faster iteration. - Improved build reliability and reproducibility by centralizing dependencies and modernizing the packaging/publishing pipeline. - Increased correctness for complex loop state scenarios in experiments, reducing risk of flaky behavior in parallel workflows. Technologies/skills demonstrated: - Python packaging and dependency management (pyproject.toml, uv integration) - CI/CD optimization (GitHub Actions, pytest-xdist) - Robust state management in Python (instance variables for loop state) - Test-driven improvements with added tests and coverage - Observability and logging enhancements across build and test pipelines Business value: - Shorter release cycles due to faster CI and more reliable builds; improved developer productivity through streamlined workflows and clearer logging; reduced risk in parallel/exhaustive test scenarios and experimental code paths.
April 2025
March 2025
1 Commits
Mar 1, 2025In March 2025, focused on stabilizing the Qblox Square Waveform Optimization in qilimanjaro-tech/qililab to address waveform reuse across multiple Play operations. Implemented a fix for improper handling of waveform durations and indices that caused incorrect Q1ASM generation, improving playback accuracy.
March 2025
1 Commits
Mar 1, 2025In March 2025, focused on stabilizing the Qblox Square Waveform Optimization in qilimanjaro-tech/qililab to address waveform reuse across multiple Play operations. Implemented a fix for improper handling of waveform durations and indices that caused incorrect Q1ASM generation, improving playback accuracy.
January 2025
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 - Summary for qilimanjaro-tech/qililab. This period focused on delivering a robust default optimization for square waveforms in the Qililab compiler, improving usability and performance while reducing configuration complexity. The change standardizes optimization behavior by auto-applying when square waveforms are detected and meet specified duration criteria, removing the need for an explicit optimization flag. This supports faster experiments and more reliable results for Qblox workflows. No major bugs were reported; CI and validation checks confirmed robustness. Key related work includes applying the default optimization (commit 24b13da72750a041c81988b93070dc7ff10513bb) in the repository.
1 Commits • 1 Features
Jan 1, 2025Month: 2025-01 - Summary for qilimanjaro-tech/qililab. This period focused on delivering a robust default optimization for square waveforms in the Qililab compiler, improving usability and performance while reducing configuration complexity. The change standardizes optimization behavior by auto-applying when square waveforms are detected and meet specified duration criteria, removing the need for an explicit optimization flag. This supports faster experiments and more reliable results for Qblox workflows. No major bugs were reported; CI and validation checks confirmed robustness. Key related work includes applying the default optimization (commit 24b13da72750a041c81988b93070dc7ff10513bb) in the repository.
January 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for qilimanjaro-tech/qililab: Delivered reliability and efficiency improvements with two major updates. Key features delivered include QM stability refinements and QbloxCompiler Square Waveform Memory Optimization. Major bugs fixed include ensuring QM is closed before reopening to prevent hanging states and removing stray log statements to reduce noise. Overall impact: increased reliability of QM workflows, reduced memory footprint for long square waveforms, and cleaner logs, enabling faster experiment throughput and easier troubleshooting. Technologies/skills demonstrated include Python resource management for hardware integration, logging hygiene, memory optimization techniques, feature-flag driven behavior, and robust configuration management. Business value: lower downtime due to hangs, reduced memory-related failures, improved operational efficiency, and clearer diagnostics.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for qilimanjaro-tech/qililab: Delivered reliability and efficiency improvements with two major updates. Key features delivered include QM stability refinements and QbloxCompiler Square Waveform Memory Optimization. Major bugs fixed include ensuring QM is closed before reopening to prevent hanging states and removing stray log statements to reduce noise. Overall impact: increased reliability of QM workflows, reduced memory footprint for long square waveforms, and cleaner logs, enabling faster experiment throughput and easier troubleshooting. Technologies/skills demonstrated include Python resource management for hardware integration, logging hygiene, memory optimization techniques, feature-flag driven behavior, and robust configuration management. Business value: lower downtime due to hangs, reduced memory-related failures, improved operational efficiency, and clearer diagnostics.
November 2024
2 Commits • 1 Features
Nov 1, 2024Monthly performance summary for 2024-11 focusing on qililab:
2 Commits • 1 Features
Nov 1, 2024Monthly performance summary for 2024-11 focusing on qililab:
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Oct 2024 monthly summary: Delivered a major library refactor and runcard modularization for qililab, enabling direct instrument bus interfaces and modular runcard settings. This work removed obsolete VNAs and drivers, simplified the Qblox sequencer class hierarchy, and eliminated system controllers, resulting in a cleaner, more maintainable architecture and a foundation for scalable instrument support.
October 2024
1 Commits • 1 Features
Oct 1, 2024Oct 2024 monthly summary: Delivered a major library refactor and runcard modularization for qililab, enabling direct instrument bus interfaces and modular runcard settings. This work removed obsolete VNAs and drivers, simplified the Qblox sequencer class hierarchy, and eliminated system controllers, resulting in a cleaner, more maintainable architecture and a foundation for scalable instrument support.
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Quality Metrics
Correctness93.6%
Maintainability89.2%
Architecture91.0%
Performance88.6%
AI Usage25.4%
Skills & Technologies
Programming Languages
JavaScriptMarkdownPythonQASMRSTShellYAML
Technical Skills
API IntegrationBackend DevelopmentBug FixingBuild Process AutomationCI/CDClass DesignClass Hierarchy SimplificationCode OrganizationCode RefactoringCompiler DevelopmentCompiler OptimizationConfiguration ManagementConfiguration RefactoringData AnalysisData Handling
Repositories Contributed To
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