{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"Provides a comprehensive toolkit for writing QUA programs, including utilities for loop parametrization, plotting, result handling, unit conversion, data analysis, and waveform generation for quantum computing experiments.","skillfed_tags":["quantum-computing","hardware-control","experiment-automation"],"use_cases":["Parametrize QUA for-loops using numpy arrays (linspace, arange, logspace) to sweep experimental parameters.","Generate and manage complex pulse sequences with nanosecond timing resolution using the Bakery framework.","Extract, analyze, and save experimental results from QUA programs, including state discrimination for two-level systems.","Convert between physical units (MHz, microseconds, millivolts) and hardware-native units in QUA programs.","Configure and control OPX hardware outputs in continuous-wave mode or as a vector network analyzer.","Perform standard single-qubit calibrations and randomized benchmarking experiments from a single Python script."],"what_it_does":"qualang-tools is a companion library for the QUA programming language, used to write and run quantum computing experiments on Quantum Machines' OPX hardware. It bundles a collection of specialized utilities: loop tools for parametrizing QUA for-loops with numpy arrays, plotting and result-handling helpers, unit conversion (MHz, microseconds, millivolts), data analysis (including state discrimination), waveform generation with nanosecond resolution, and configuration management for the OPX control system.\n\nThe package is organized into modules covering different aspects of quantum experiment workflow\u2014from low-level waveform design and hardware control to high-level experiment orchestration and multi-user coordination. It also includes integrations with external frameworks like QCoDeS and tools for calibration, dynamic parameter tuning (video mode), and digital filter design. Most users will import specific submodules (e.g., loops, plot, results, units) rather than the entire package.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, installs cleanly, and supports current Python versions. It is purpose-built for QUA users and provides essential utilities that would otherwise require custom implementation. Install it if you are writing QUA programs; skip it if you do not use Quantum Machines hardware."},"id":"qualang-tools","links":{"html":"https://skillfed.io/packages/qualang-tools","md":"https://skillfed.io/packages/qualang-tools.md","pypi":"https://pypi.org/project/qualang-tools/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-31","license_spdx":null,"license_treatment":"permissive","name":"qualang-tools","python_support":"supports_current","summary":"The qualang_tools package includes various tools related to QUA programs in Python"},"popularity":{"monthly_downloads":121982,"position":11965,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.23.0"}