---
id: cirq
version: "1.7.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# cirq — A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.

License: permissive · Maintenance: active · Downloads: 162.7K/mo

## 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 above — verify before relying.

Cirq is a Python framework for writing, manipulating, and running quantum circuits on quantum computers and simulators, with support for parameterized circuits, noise modeling, and hardware device abstractions.

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

pip install cirq
uv add cirq
poetry add cirq

## Installing cirq

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.

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)

Requires Python 3.11 or later.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 162.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

quantum circuit programming, quantum simulator python, nisq quantum computing, quantum algorithm development, quantum circuit optimization, quantum hardware abstraction, parameterized quantum circuits, quantum-computing, circuit-simulation, nisq

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