cirq-core
A framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
Decision gist · record as of 2026-08-14
Yes. Cirq-core is a production-stable, actively maintained framework with no known vulnerabilities, low install friction, and a permissive license. It is the right choice if you need to write and simulate quantum circuits for NISQ research or development. Install it if you're working on quantum algorithms or need a Python-based quantum computing toolkit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11.0 or later; numpy, scipy, and sympy are runtime dependencies for numerical and symbolic computation.
- Low friction installation with a pure Python wheel.
- Actively maintained with a recent release (46 days old) and ongoing repository activity.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions; you must include a copy of the license and notice of modifications.
last release 2026-06-29 (46 days) · last repo commit 2026-08-14 · 5,046 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 523,595 downloads/mo, #6,194 on PyPI
Alternatives
Verify before relying
pip install cirq-core
import cirq
# Create a simple quantum circuit
q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
cirq.H(q0),
cirq.CNOT(q0, q1)
)
print(circuit)- Whether the built-in simulators support GPU acceleration or are CPU-only.
- Performance characteristics for circuits with large qubit counts or deep gates.
- Compatibility with specific quantum hardware platforms beyond the core framework.
What it is and what it does
Cirq-core is the foundational Python framework for designing and simulating quantum circuits on NISQ devices. It abstracts away hardware-specific details while keeping them accessible when needed, allowing researchers and developers to write quantum algorithms that can run on simulators or be adapted for real quantum hardware. The package provides circuit construction primitives, gate operations, measurement tools, and a built-in simulator for testing algorithms before deployment.
The package depends on standard scientific Python libraries—numpy, scipy, sympy, pandas, networkx, matplotlib—for numerical computation, symbolic algebra, data handling, and visualization. It's designed as a modular core that can be extended with hardware-specific interface modules for different quantum computing platforms. Development is active, with recent releases and ongoing maintenance.
Use it for
- Prototyping quantum algorithms and testing them on a local simulator before submitting to real quantum hardware.
- Teaching quantum computing concepts and quantum algorithm design in educational settings.
- Researching NISQ algorithms and exploring quantum circuit optimization techniques.
- Building quantum applications that need to run on multiple quantum platforms via Cirq's hardware abstraction layer.
- Analyzing and visualizing quantum circuits to understand their structure and behavior.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Cirq-core is a production-stable, actively maintained framework with no known vulnerabilities, low install friction, and a permissive license. It is the right choice if you need to write and simulate quantum circuits for NISQ research or development. Install it if you're working on quantum algorithms or need a Python-based quantum computing toolkit.
Install
cirq-core on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained with a recent release (46 days old) and ongoing repository activity. Requires Python 3.11 or later.
Requires Python 3.11.0 or later; numpy, scipy, and sympy are runtime dependencies for numerical and symbolic computation.
License in practice
Apache-2.0 permissive license allows use in commercial and private projects with minimal restrictions; you must include a copy of the license and notice of modifications.
Quickstart
pip install cirq-core
import cirq
# Create a simple quantum circuit
q0, q1 = cirq.LineQubit.range(2)
circuit = cirq.Circuit(
cirq.H(q0),
cirq.CNOT(q0, q1)
)
print(circuit)Verify before relying
- Whether the built-in simulators support GPU acceleration or are CPU-only.
- Performance characteristics for circuits with large qubit counts or deep gates.
- Compatibility with specific quantum hardware platforms beyond the core framework.
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 | 9 packagesattrsduetmatplotlibnetworkxnumpypandasscipysympytqdm |
| Maintenance | Actively maintained 46 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 523,595 / month, #6,194 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_core-1.7.0-py3-none-any.whl
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See also cirq · cirq-google · cirq-pasqal · cirq-web · pytket · cirq-aqt · cirq-ionq · hugr · pennylane · qiskit-aer