qiboteam/qibo
Oct 2024 – Oct 2025
6 Months active
Languages Used
PythonRSTTOMLrst
Technical Skills
Backend DevelopmentCode RefactoringDependency ManagementPyTorchQuantum ComputingTensorFlow
MatteoRobbiati
PROFILE
Matteorobbiati
Matteo Robbiati contributed to the qiboteam/qibo repository by developing and refining backend infrastructure, data visualization tools, and documentation to support quantum computing workflows. He unified PyTorch backend integration under the qiboml platform, modernized dependency management using Python and TOML, and improved test reliability across evolving backends. Matteo enhanced circuit execution insight by building interactive visualization modules with Matplotlib and strengthened reproducibility through robust numerical computation fixes. He also improved documentation quality by integrating scholarly citations and collaboration references. His work demonstrated depth in backend development, packaging hygiene, and UI testing, resulting in more stable, maintainable, and research-friendly code.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
31Total
Bugs
4
Commits
31
Features
7
Lines of code
2,433
Activity Months6
Your Network
19 peopleWork History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Key feature delivered — new Documentation: Collaboration Papers Section and Citations in qibo docs, including a Bell inequalities paper with journal publication and arXiv links to boost discoverability for researchers using the Qibo project. No major bugs fixed this month. Impact: improved research discoverability, stronger documentation quality, and enhanced collaboration potential for Qibo. Demonstrated skills: documentation, version control, scholarly citation integration, and cross-team collaboration.
1 Commits • 1 Features
Oct 1, 2025October 2025: Key feature delivered — new Documentation: Collaboration Papers Section and Citations in qibo docs, including a Bell inequalities paper with journal publication and arXiv links to boost discoverability for researchers using the Qibo project. No major bugs fixed this month. Impact: improved research discoverability, stronger documentation quality, and enhanced collaboration potential for Qibo. Demonstrated skills: documentation, version control, scholarly citation integration, and cross-team collaboration.
October 2025
June 2025
2 Commits
Jun 1, 2025June 2025: Focused on numerical reliability in Hamiltonian expectation value calculations for qiboteam/qibo. Implemented robust casting and real-valued results to prevent numerical instability and maintain correctness in simulations. No new features released this month; primary work targeted bug fixes and code hygiene to improve reliability and accuracy for downstream users.
June 2025
2 Commits
Jun 1, 2025June 2025: Focused on numerical reliability in Hamiltonian expectation value calculations for qiboteam/qibo. Implemented robust casting and real-valued results to prevent numerical instability and maintain correctness in simulations. No new features released this month; primary work targeted bug fixes and code hygiene to improve reliability and accuracy for downstream users.
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for qiboteam/qibo: Delivered core visualization capabilities for circuit execution and state visualization, strengthened UI test coverage, and fixed key reliability bugs. Key features delivered include a Circuit Execution Visualizer with interactive plots (state amplitudes, probabilities, measurement frequencies) and refactored drawing utilities; default state visualization now shows all relevant components where feasible with updated docs and visualize_state usage; UI test suite for result visualization was enhanced with additional multi-qubit data and test utilities fixes. Major bugs fixed include updating the default argument for n_most_relevant_component and fixes to test imports and numpy backend seeds that improved test reliability. Overall impact: reduced debugging time, improved circuit insight, and more dependable multi-qubit visualization; Technologies/skills demonstrated: Python, plotting UI, plotting utilities refactoring, test-driven development, numpy backend handling, and documentation updates.
15 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary for qiboteam/qibo: Delivered core visualization capabilities for circuit execution and state visualization, strengthened UI test coverage, and fixed key reliability bugs. Key features delivered include a Circuit Execution Visualizer with interactive plots (state amplitudes, probabilities, measurement frequencies) and refactored drawing utilities; default state visualization now shows all relevant components where feasible with updated docs and visualize_state usage; UI test suite for result visualization was enhanced with additional multi-qubit data and test utilities fixes. Major bugs fixed include updating the default argument for n_most_relevant_component and fixes to test imports and numpy backend seeds that improved test reliability. Overall impact: reduced debugging time, improved circuit insight, and more dependable multi-qubit visualization; Technologies/skills demonstrated: Python, plotting UI, plotting utilities refactoring, test-driven development, numpy backend handling, and documentation updates.
March 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Key outcomes include dependency hygiene improvements and test reliability enhancements that streamline Linux deployments and reduce maintenance overhead. Delivered a streamlined install experience by removing TensorFlow from dependencies and stabilized CI tests after dependency changes. Impact: faster onboarding, fewer flaky tests, and more robust packaging across Linux environments. Technologies/skills demonstrated: Python packaging and dependency management with Poetry, packaging hygiene, test stability, and Linux environment considerations. Business value: lower install friction, more reliable deployments, and easier cross-platform support.
December 2024
2 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Key outcomes include dependency hygiene improvements and test reliability enhancements that streamline Linux deployments and reduce maintenance overhead. Delivered a streamlined install experience by removing TensorFlow from dependencies and stabilized CI tests after dependency changes. Impact: faster onboarding, fewer flaky tests, and more robust packaging across Linux environments. Technologies/skills demonstrated: Python packaging and dependency management with Poetry, packaging hygiene, test stability, and Linux environment considerations. Business value: lower install friction, more reliable deployments, and easier cross-platform support.
November 2024
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for qiboteam/qibo. Key work focused on backend provider migration, dependency modernization, and test alignment with backend removal. These actions enhance stability, compatibility with newer Python environments, and clarity of backend usage for developers and users.
6 Commits • 2 Features
Nov 1, 2024November 2024 monthly summary for qiboteam/qibo. Key work focused on backend provider migration, dependency modernization, and test alignment with backend removal. These actions enhance stability, compatibility with newer Python environments, and clarity of backend usage for developers and users.
November 2024
October 2024
5 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for qiboteam/qibo: focused on consolidating the PyTorch backend into the Qibo ML backend under the qiboml platform, updating the test suite to support the new backend (qiboml-pytorch), and aligning dependencies for a stable qibot build. Completed backend unification, test migration, and import-path fixes to enable smoother PyTorch-backed experimentation and faster delivery of machine learning features.
October 2024
5 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for qiboteam/qibo: focused on consolidating the PyTorch backend into the Qibo ML backend under the qiboml platform, updating the test suite to support the new backend (qiboml-pytorch), and aligning dependencies for a stable qibot build. Completed backend unification, test migration, and import-path fixes to enable smoother PyTorch-backed experimentation and faster delivery of machine learning features.
Activity
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Quality Metrics
Correctness90.6%
Maintainability92.8%
Architecture90.4%
Performance85.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonRSTTOMLrst
Technical Skills
Backend ConfigurationBackend DevelopmentCode RefactoringData VisualizationDebuggingDependency ManagementDocumentationMatplotlibNumerical ComputationPyTorchPythonPython DevelopmentPython PackagingQuantum ComputingRefactoring
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