---
id: qiskit-experiments
version: "0.14.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# qiskit-experiments — Software for developing quantum computing programs

License: permissive · Maintenance: active · Downloads: 131.1K/mo

## 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 above — verify before relying.

Qiskit Experiments provides tools for building, running, and analyzing experiments on noisy quantum computers using the Qiskit framework.

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

pip install qiskit-experiments
uv add qiskit-experiments
poetry add qiskit-experiments

## Installing qiskit-experiments

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).

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 experiments

Requires Python 3.10 or later; Qiskit and qiskit-ibm-runtime must be installed and configured to connect to quantum backends.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 131.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

quantum experiment framework, qiskit quantum computing, noisy quantum computer analysis, quantum circuit experiments, qiskit experiment tools, quantum data analysis, quantum characterization, quantum-computing, qiskit-ecosystem

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