October 2025: Delivered cross-repo features across qibo, qibolab_platforms_qrc, and qibocal, with a focus on documentation, standardization, calibration workflows, and reliability improvements. Key outcomes include D-notation naming and calibration.json groundwork, ported calibration framework with line D and single-qubit calibration, enhanced protocol execution visibility with guaranteed output saving, and strengthened testing and CI by adding calibration script tests and linting exemptions. These changes improve reproducibility, onboarding, and the overall quality of the quantum control stack.

September 2025 monthly summary for qiboteam repositories. Delivered architectural enhancements and feature deliveries across qibo and qibolab, with a strong focus on robust gradient-enabled gate control and flexible signal processing pipelines. Key outcomes include a Gate Abstraction Refactor with gradient support and a gradient validation test, a comprehensive and extensible Filters subsystem for qibolab with a preliminary API, Pydantic-based models, and hardware-aware configurations; plus targeted bug fixes to ensure reliable filter behavior on hardware. These efforts improve maintainability, enable higher-fidelity quantum control and signal processing, and lay groundwork for expanded hardware support.

August 2025: Delivered a set of high-impact enhancements across qibocal, qibolab, and qibo that improve measurement fidelity, readout robustness, and optimization workflows. The month emphasized concrete business value: more accurate TOF measurements, richer readout characterization, cleaner emulator codebase, and gradient-enabled optimization for faster, more reliable parameter tuning.

July 2025 saw substantial stability, accuracy, and productivity gains across qibolab and qibocal. A major emulator refactor unified Hamiltonian modeling and configuration propagation, with Align support and lazy processing that improves robustness and performance. Targeted bug fixes and plotting improvements enhanced simulation correctness and measurement visualization. In qibocal, Time-of-Flight readout enhancements and resonator data improvements delivered more configurable, reliable readouts and clearer data visualization. Documentation updates improved onboarding and emulator usage. Collectively, these changes boost data integrity, reduce debugging time, and enable scalable, business-ready quantum experiments.

June 2025 performance summary focusing on delivering core experimental capabilities, enabling multi-lab collaboration, and strengthening reliability across qibocal, qibolab, and qibo. Key initiatives include introducing and extending T1/T2 flux experiments with robust data acquisition, fitting, plotting, and documentation; generalizing report upload to support multiple laboratories with lab-specific configurations; refactoring and hardening calibration reporting by applying decibel (dB) scaling; fixing critical readout labeling and acquisition flow issues; and adding CMA backend safety checks to improve reliability for optimization workflows. The month also includes emulator documentation improvements and iSWAP support integration in the compiler, highlighting cross-team collaboration and comprehensive testing. This summary highlights the business value: improved data quality and reproducibility, scalable lab reporting, streamlined workflows, and stronger reliability in optimization and calibration across the stack.

May 2025 monthly summary for performance review: Overall, the month focused on architectural alignment, expanding calibration capabilities, and increasing test coverage to improve reliability, maintainability, and business value across qibocal and qibolab. Deliverables targeted long-term stability, cross-module coherence, and scalable calibration workflows while enhancing developer productivity and CI readiness. 1) Key features delivered - Import coherence across modules in qibocal to stabilize builds and reduce import errors (commits 10bab2061896381e581e910fc8774f1982dad326; 216c08991528c67059f348a4d375444ef69a81dc). - Moved cryoscope module into flux protocols to better align architecture (commit a37b056112b65b56bae571237c17d3bb84f51065). - TWPA calibration protocol with improved fitting, edge-case handling, gain calculation fix, magnitude processing, and documentation (commits 25be70c0e61b348196694716a8c2bd045ef0305e; 718bd03404952b78f19e3af60600ea71ad4ee97d; b1c98475e8c2e2f3cc31fcd355eb76a3f0898186; 70fa48dcf5b9163f9d2ac427e16d3a2f68b9b8fb; 0264663939e830e154fe5bc181a19fbf86214b84; c7a8f7859cf1aaa90607cbf37e04070a2afbedba). - Coupler architecture prototype and multi-qubit results retrieval using numpy (commits 6b23bed335bb918d061be29f4c37f560aa6aafec; 12a38e3e0af3f91ac58f2707d743f15be93ef832). - Voltage_to_flux integration and propagation to the coupler, enabling end-to-end control (commits 8fc6c23d684cf38d087e591da205e5615e7d6482; a772bee583ae98b525e804628e738254c41a9587). 2) Major bugs fixed - Restored correct results and Python 3.9 compatibility in the qibolab implementation; dephasing operator fixes and cleanup (commits 1de391f09f1c701fa3dff6fce8447dc29ee64ca5; 5f387cc1c0072ce10d48fd38db6dd253efcfdaf4; 983e90c2e458a891fc100859a7553b4683e9b968; 7662e39d9de8455a8a981572a11b2ad6fb28cf51). - Fixes to documentation tests and to calibration-related test stability (commits aa4b7f8bd669937bd5b2ed48358c5aa5b0f6682e; 37c51955a6f66d4e14c55893d9a52900049f0617; aacb61bac86965e89fdf8123a9deeb88b0b79bd3; d2a97e0d4145fad50a10498431c9eabeedf5e39c). - Removal of a duplicate term in the coupler model to ensure physical consistency (commit 9740462a2c444555c6e7acdd9c4c90f13bcbbba9). - Stabilized test suite and post-merge fixes to improve overall test reliability (commit 37c51955a6f66d4e14c55893d9a52900049f0617; b6e6d091350b8813cdae31c3c24bc344e4a05711). 3) Overall impact and accomplishments - Achieved a more maintainable and scalable architecture by consolidating imports, aligning cryoscope with flux protocols, and formalizing calibration workflows across multiple qubits. - Expanded test coverage and documentation to reduce regression risk and accelerate onboarding, enabling faster iteration and safer releases. - Improved build stability and compatibility with updated tooling and libraries (Qibolab, numpy, sphinx), supporting longer-term maintainability and vendor support. - Established end-to-end calibration paths (TWPA, multi-qubit calibration, voltage-to-flux integration) that enable more accurate, data-driven qubit control and measurement pipelines. 4) Technologies/skills demonstrated - Python refactoring, modular architecture, and cross-module coherence for large-scale quantum control stacks. - Calibration algorithms, fitting workflows, edge-case handling, and robust magnitude processing for TWPA and multi-qubit calibration. - Coupler-based architecture development, multi-qubit results retrieval using numpy, and integration of voltage-to-flux control paths. - Documentation, testing, and CI tooling upgrades (Sphinx, pytest-style tests), with attention to reproducibility and builder stability.

April 2025 monthly summary: Strengthened reliability and coherence across the quantum development stack through documentation improvements, naming consistency, dependency alignment, and expanded emulator testing. Delivered flux amplitude workflow docs for qibocal, standardized drive_qudits to drive_extra naming across code and platforms, and upgraded Qibolab/Qibo dependencies with a locked Qibolab 0.2.5. Expanded emulator test coverage to qutrit/qubit platforms, circuits, Hamiltonians, and integration mode, plus emulator onboarding docs and public TU instruments. Result: reduced maintenance overhead, fewer flaky tests, and faster hardware iteration cycles.

March 2025 monthly summary: Focused on stability, extensibility, and maintainability across qibocal and qibolab. Delivered new capabilities, improved documentation and testing, and applied platform and protocol fixes to reduce errors and broaden hardware support. Key outcomes include ZZ protocol coupling, generalized n-level Hamiltonians, Virtual Z support in the emulator, and infrastructure improvements (DriveConfig, QubitPairMap, lockfile updates). These efforts improve experiment reliability, onboarding velocity, and future-proofing of the stack.

February 2025 performance summary: Delivered multi-repo improvements across qibo, qibocal, and qibolab, focusing on GPU-enabled execution, backend flexibility, platform loading, and test reliability. Key features delivered include Qibo Qibojit integration with GPU backend compatibility, MetaBackend integration and local-backend simplifications in qibocal, and emulator/platform-loader refactors in qibolab. Major bugs fixed include parking timing and drive_delay in the parking system and emulator robustness improvements (probability normalization), as well as targeted test fixes to address cluster reliability and edge cases. These efforts yielded improved runtime performance on GPU, more robust backend path selection, simplified platform loading, and expanded test coverage. Technologies demonstrated include Python-based backend abstractions, GPU-accelerated backends, platform loading mechanisms, test automation, code refactoring for compatibility, and comprehensive documentation updates.

January 2025 performance summary for the qiboteam: Delivered feature-rich enhancements and reliability improvements across qibolab, qibocal, and platform components, delivering business value through more realistic simulations, scalable emulation, and stronger build/test readiness. Major progress includes: 1) sweeping and result handling enhancements enabling single-shot acquisition, output shaping for swept parameters, and result merging across plays; 2) decoherence modeling for Qubit (T1/T2) with updated Hamiltonian calculations and unit conversions for realistic quantum simulations; 3) emulator architecture improvements with unrolling, multi-sequence support, and three-state (qutrit) extension; 4) emulator/back-end integration and protocol work in qibocal, including flux crosstalk protocol prototype and build readiness for Python 3.12 and skops 0.11.0; 5) platform alignment via Platform Parameter Porting to version 0.2 in qibolab_platforms_qrc. In addition, robustness and testing improvements across the stack address control-flow and timing edge cases to stabilize releases and reduce troubleshooting time for experiments.

December 2024 monthly summary focusing on key accomplishments across qibocal and qibolab. Delivered end-to-end protocol capabilities for coherence measurements, UI improvements, and enhanced testing capabilities, with a focus on business value and code quality.

November 2024 (2024-11) monthly summary for qibocal, qibolab_platforms_qrc, and qibolab. Delivered robust readout and calibration tooling, protocol ports, and platform reliability improvements across multiple repos. Focused on increasing scientific throughput, accuracy of calibrations, and code quality to support faster experimentation and production-grade stability.

October 2024 — Qubit control modernization, calibration data modeling, and platform reliability drive stronger business value. Delivered a library 0.2 upgrade across qibocal for core qubit controls (T1/T2/Zeno, dispersive shift, flipping) with UI alignment; DRAG pulse accuracy improvements via dynamic anharmonicity retrieval and correct beta application for negative amplitudes; enhanced calibration and readout data handling with uncertainties, new coupling and Josephson energy fields, improved readout characterization, and data integrity tests; platform loading robustness by introducing locate_platform and reducing environment-variable reliance; calibration data refactor and updates for qibolab_platforms_qrc to improve clarity and reliability. Also completed tests and data serialization enhancements to support robust experimentation. Business impact includes higher fidelity control, faster experiment setup, and reduced maintenance burden. Technologies demonstrated: protocol refactors, data modeling with uncertainties, dynamic calibration integration, test-driven approaches, and platform discovery improvements.