July 2025 — Monthly summary for qiboteam/qibocal. Key features delivered include Time-of-Flight Readout Improvements and Calibration (baseline TOF, threshold-based onset detection for TOF fitting, renamed timing field to time_of_flights, and integration of readout/channel delays into the pulse execution flow to improve timing accuracy and display). Also delivered Qubit Model Saving and Fitting Cleanup (fix for fit_virtualz parameter handling, stable model dumping with skops.io, re-enabled model persistence, and removal of unnecessary debug prints and redundant returns). Plotting Enhancements for Virtual Z Phases (refactored plotting, removal of redundant prints, and a guard to skip plotting when the setup is not 'I' to improve performance and user experience). Mock Platform and GaussianSquare Parameter Corrections (correct initialization of GaussianSquare parameters to ensure proper pulse sequence initialization). Dependency Upgrades for Stability and Compatibility (updated core dependencies to latest compatible versions to improve stability and access to bug fixes). Code Cleanup and Refactoring (cleanup SNZ optimization utilities by removing commented-out code and unused functions, and clarifying TODOs for future work). Overall, these changes improve timing accuracy, model reliability, plotting performance, platform initialization reliability, and system stability, enabling faster experimentation and more reproducible results. Technologies/skills demonstrated include Python development, persistence with skops.io, plotting optimization, dependency management, and code quality improvements.

June 2025 Monthly Summary: Delivered cross-repo features and stability improvements across qibocal and qibolab, with a clear impact on control fidelity, benchmarking, and maintainability. Highlights include SNZ optimization overhaul, extended RB fitting for multi-qubit targets, improved GaussianPulse generation in Qibolab, and targeted bug fixes that increase reliability across protocols. Key achievements: - SNZ optimization protocol overhaul: configurable flux_time_delay, modernized finetuning parameters, and enhanced acquisition/fitting pipelines, with refactors to fit functions and broader protocol propagation across related modules. (Commits including changes to flux pulse delay, theta parameter removal, and fit function abstractions.) - Randomized Benchmarking (RB) fit generalized for multi-qubit targets: enabling higher-dimensional fidelity measurements and scalable RB analysis. - GaussianPulse generation improvements and normalization (GaussianSquare) in Qibolab: improved normalization propagation, type hints, sampling logic, and integration of normalization methods for consistent Gaussian pulses across experiments. - Chevron protocol bug fixes and stability improvements: corrected ground-state probability calculations after state mappings and gate additions, and updates to registered qubit handling; ensured consistency with protocol changes. - Dependency stability: reverted qibolab dependency to stabilize the project’s dependency tree during this period. Impact and business value: - Improved control fidelity and reliability for multi-qubit experiments, enabling more accurate benchmarking and faster iteration cycles. - Greater maintainability through refactors, type hints, and documentation, reducing onboarding time and future risk. - Stabilized build and environment across core repos, lowering integration risk for downstream experiments and CI pipelines. Technologies and skills demonstrated: - Python-based protocol engineering, multi-repo collaboration, code refactoring, and test-driven improvements. - Advanced data handling for quantum calibration workflows, normalization strategies, and dimension-generalized RB fitting. - Emphasis on documentation, code quality, and maintainability across complex experimental protocols.

May 2025 monthly summary for qibocal (qiboteam/qibocal). This period focused on protocol robustness, dependency stability, and maintainability, delivering concrete business value through more reliable experiments, streamlined setup, and cleaner data visualization.

April 2025 monthly summary for two repositories (qibolab, qibocal), highlighting delivered features, major bug fixes, overall impact, and skills demonstrated. Focused on delivering business value through more robust pulse shaping, gate calibration, and clearer roadmaps, complemented by documentation and test coverage improvements.

March 2025 monthly summary for qibocal highlighting Virtual Z-phase protocol work focused on reliability, performance, and maintainability. Delivered timing and pulse handling enhancements for Virtual Z-phase, refined delay calculations, and streamlined flux-channel selection to improve measurement reliability. Completed protocol documentation and code cleanup to reduce debugging noise and improve developer experience. Overall, these changes reduce measurement errors, shorten troubleshooting time, and strengthen the product’s scientific accuracy and robustness across deployments.

February 2025 delivered reinforced reliability, user-facing flexibility, and stronger developer experience for qibocal. Key features span user-defined gate customization, API/test consistency improvements for Qibolab compatibility, and comprehensive documentation updates. Major bugs were resolved to improve cross-platform correctness, notably platform-native gates handling in unrolling and alignment between platform and backend, plus a pulses-aware compiler path. Build, CI, and code-quality efforts reduced maintenance overhead and stabilized the release process, enabling faster, safer deployments. The combined impact is a more flexible gate-assembly workflow for users and a more maintainable, scalable codebase.

Monthly summary for 2025-01 focused on delivering robust data analysis, visualization improvements, and code maintainability in qibocal. Key work includes fixes to prevent data corruption in qubit flux dependence fitting, robustness enhancements for sweetspot calculation with median-based statistics and updated docs, addition of a sweetspot visualization trace, and naming standardization across resonator frequency reporting. These changes were implemented through eight commits across four work items, delivering clearer results, improved reliability, and better long-term maintainability.

Month 2024-12 Performance Summary: Across qiboteam/qibo and qiboteam/qibocal, delivered reliability improvements, targeted maintenance, and workflow simplifications that reduce technical debt while preserving business value. Focused on robust graph initialization, removal of deprecated features, and streamlined contribution processes to support faster, safer iterations and clearer product scope.

November 2024 performance summary for qiboteam/qibocal focused on delivering robust data modeling, refactoring, and reliability improvements that translate into measurable business value. Key features delivered include a comprehensive Mermin refactor and data modeling overhaul, introduction of STRING_TYPE and a defined targets property to improve data integrity, and a refactored readout mitigation matrix acquisition and post-processing process to enhance accuracy and maintainability. In addition, maintenance and docs work consolidated around deprecation notices for qq auto and removal of obsolete action_qq.yml to reduce technical debt. Major bug fixes addressed plotting and merging stability, lint hygiene, variable naming, mitigation outputs handling, 2-qubit circuit usage, and platform connectivity post-execution, resulting in fewer runtime errors and more predictable results. Overall impact: Delivery of core data quality improvements, more reliable simulation results, and clearer documentation, enabling faster onboarding, easier maintenance, and stronger confidence in production deployments. These efforts reduce risk in future experiments and support scale-up of qibocal workflows. Technologies/skills demonstrated: Python refactoring, robust data modeling, type safety enhancements, documentation discipline, CI-friendly code quality improvements, and end-to-end stabilization of circuit execution and mitigation workflows.

October 2024 monthly summary for qibocal (qiboteam). Key work focused on documentation modernization and data structure cleanup, delivering measurable business value through clearer guidance and simplified optimization workflows. Highlights: Chevron Protocol Documentation Modernization (deprecate spectroscopy docs, update parameter path references, improve readability) and Two-Qubit Optimization Data Cleanup (remove unused vphases and related attributes). Impact: improved onboarding and reduced maintenance burden; faster debugging and fewer misconfigurations. Skills: Python-based refactoring, Sphinx/reST documentation, data-model hygiene, cross-team collaboration.