cirq
A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
Decision gist · record as of 2026-08-14
Yes. Cirq is a mature, actively maintained framework (release 46 days ago, 5046 GitHub stars) with no known vulnerabilities, permissive Apache 2.0 licensing, and low install friction. It is the right choice if you are building quantum algorithms, simulating circuits, or preparing code for real quantum hardware. Install it if quantum computing development is your goal.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Low install friction with a pure Python wheel.
- Active maintenance with a release 46 days ago and ongoing repository activity.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial and private use without restriction. You can use, modify, and distribute Cirq freely as long as you include the license notice.
last release 2026-06-29 (46 days) · last repo commit 2026-08-14 · 5,046 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 162,654 downloads/mo, #10,593 on PyPI
Alternatives
Verify before relying
pip install cirq
import cirq
qubit = cirq.GridQubit(0, 0)
circuit = cirq.Circuit(
cirq.X(qubit)**0.5,
cirq.measure(qubit, key='m')
)
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=20)
print(result)- Performance characteristics and simulation speed limits compared to specialized simulators.
- Compatibility and integration details with specific quantum hardware providers beyond those listed.
- Memory requirements for large circuits or high repetition counts.
What it is and what it does
Cirq is a Python framework for designing and simulating quantum circuits, built to handle the constraints and opportunities of today's noisy intermediate-scale quantum (NISQ) computers. It provides abstractions for quantum gates, qubits, and measurements, along with tools for circuit transformation, optimization, and noise modeling. The package bundles integrations with multiple quantum hardware providers (Google, AQT, IonQ, Pasqal) and includes built-in simulators for testing circuits before running them on real hardware.
You use Cirq to define quantum circuits programmatically, apply gates and measurements, simulate their behavior, and prepare them for execution on quantum computers or high-performance simulators. It interoperates with NumPy and SciPy, supports parameterized circuits with symbolic variables, and offers hardware device modeling so you can account for real device constraints during development. The framework is actively maintained, well-documented, and used in research and production quantum computing workflows.
Use it for
- Design and test quantum algorithms before running them on real quantum hardware.
- Simulate quantum circuits with noise models to study error effects on NISQ devices.
- Build parameterized circuits with symbolic variables for variational quantum algorithms.
- Develop cross-platform quantum applications that work with multiple hardware providers.
- Optimize and compile quantum circuits for specific hardware constraints and gate sets.
- Prototype fault-tolerant quantum algorithms and error correction schemes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Cirq is a mature, actively maintained framework (release 46 days ago, 5046 GitHub stars) with no known vulnerabilities, permissive Apache 2.0 licensing, and low install friction. It is the right choice if you are building quantum algorithms, simulating circuits, or preparing code for real quantum hardware. Install it if quantum computing development is your goal.
Install
cirq on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance with a release 46 days ago and ongoing repository activity. Requires Python 3.11 or later. Six runtime dependencies (cirq-core, cirq-google, cirq-aqt, cirq-ionq, cirq-pasqal, cirq-web) are bundled, so installation is straightforward.
Requires Python 3.11 or later.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial and private use without restriction. You can use, modify, and distribute Cirq freely as long as you include the license notice.
Quickstart
pip install cirq
import cirq
qubit = cirq.GridQubit(0, 0)
circuit = cirq.Circuit(
cirq.X(qubit)**0.5,
cirq.measure(qubit, key='m')
)
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=20)
print(result)Verify before relying
- Performance characteristics and simulation speed limits compared to specialized simulators.
- Compatibility and integration details with specific quantum hardware providers beyond those listed.
- Memory requirements for large circuits or high repetition counts.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.11.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagescirq-aqtcirq-corecirq-googlecirq-ionqcirq-pasqalcirq-web |
| Maintenance | Actively maintained 46 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 162,654 / month, #10,593 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering :: Quantum ComputingTopic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: cirq-1.7.0-py3-none-any.whl
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See also cirq-core · cirq-web · cirq-google · cirq-pasqal · pytket · cirq-aqt · pennylane · hugr · stim · cirq-ionq