qiskit-experiments
Software for developing quantum computing programs
Decision gist · record as of 2026-08-14
Yes, if you are actively developing or researching quantum computing with Qiskit and IBM quantum hardware. The package is actively maintained, has no known vulnerabilities, and low install friction. It is well-suited for quantum researchers and engineers. Not necessary if you only use Qiskit for circuit simulation or do not need structured experiment management.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; Qiskit and qiskit-ibm-runtime must be installed and configured to connect to quantum backends.
- Low install friction with a pure-Python wheel.
- Active maintenance with a recent release 73 days ago and commits through 2026-08-12.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both research and production quantum computing workflows.
last release 2026-06-02 (73 days) · last repo commit 2026-08-12 · 196 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 131,089 downloads/mo, #11,609 on PyPI
Alternatives
Verify before relying
pip install qiskit-experiments
from qiskit_experiments import Experiment
from qiskit import QuantumCircuit
# Use Experiment classes to build and analyze quantum experiments- Specific experiment types and analysis capabilities beyond the generic 'building, running, analyzing' description.
- Whether the package includes pre-built experiment templates or requires custom implementation.
- Integration scope with IBM Quantum services beyond what qiskit-ibm-runtime provides.
What it is and what it does
Qiskit Experiments is a specialized toolkit for quantum computing researchers and developers who need to design and execute experiments on real noisy quantum hardware. It sits on top of the Qiskit SDK and qiskit-ibm-runtime, providing higher-level abstractions for experiment design, execution, and result analysis. The package depends on the full scientific Python stack (numpy, scipy, pandas, matplotlib, uncertainties, lmfit) to handle numerical computation, statistical analysis, and visualization of quantum measurement data.
The package is designed for users working with IBM quantum computers or simulators through Qiskit, who need structured tools to characterize quantum systems, calibrate gates, or validate quantum algorithms on real hardware. It abstracts away boilerplate experiment management code and provides built-in analysis routines for common quantum characterization tasks.
Use it for
- Characterizing noise properties and gate errors on quantum hardware to improve circuit optimization.
- Running calibration experiments to tune quantum gate parameters for better fidelity.
- Analyzing measurement results from quantum circuits with built-in statistical tools.
- Validating quantum algorithms on noisy quantum computers before scaling up.
- Building reproducible quantum computing workflows with structured experiment templates.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively developing or researching quantum computing with Qiskit and IBM quantum hardware.
The package is actively maintained, has no known vulnerabilities, and low install friction. It is well-suited for quantum researchers and engineers. Not necessary if you only use Qiskit for circuit simulation or do not need structured experiment management.
Install
qiskit-experiments on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a recent release 73 days ago and commits through 2026-08-12. Ten runtime dependencies including core scientific stack (numpy, scipy, pandas) and Qiskit ecosystem packages (qiskit, qiskit-ibm-runtime).
Requires Python 3.10 or later; Qiskit and qiskit-ibm-runtime must be installed and configured to connect to quantum backends.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for both research and production quantum computing workflows.
Quickstart
pip install qiskit-experiments
from qiskit_experiments import Experiment
from qiskit import QuantumCircuit
# Use Experiment classes to build and analyze quantum experimentsVerify before relying
- Specific experiment types and analysis capabilities beyond the generic 'building, running, analyzing' description.
- Whether the package includes pre-built experiment templates or requires custom implementation.
- Integration scope with IBM Quantum services beyond what qiskit-ibm-runtime provides.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesnumpyscipyqiskitqiskit-ibm-runtimematplotlibuncertaintieslmfitrustworkxpandaspackaging |
| Maintenance | Actively maintained 73 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 131,089 / month, #11,609 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: qiskit_experiments-0.14.1-py3-none-any.whl
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See also qcodes · qiskit · qiskit-algorithms · qiskit-terra · qiskit-aer · samplomatic · cirq-core · qualang-tools · qiskit-ibm-runtime · bluesky