cirq-core

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

Cirq-core provides the foundational framework for writing, manipulating, and simulating quantum circuits on noisy intermediate-scale quantum (NISQ) devices, with built-in simulators and abstractions for quantum algorithm development.

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

pip install cirq-core

uv add cirq-core

poetry add cirq-core

Installing cirq-core

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.

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)

Requires Python 3.11.0 or later; numpy, scipy, and sympy are runtime dependencies for numerical and symbolic computation.

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

Evidence: cirq_core-1.7.0-py3-none-any.whl

Keywords: algorithms, api, cirq, google, google quantum, nisq, python, quantum, quantum algorithms, quantum circuit, quantum circuit simulator, quantum computer simulator, quantum computing, quantum development kit, quantum information, quantum programming, quantum programming language, quantum simulation, sdk, simulation

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