Cirq

Project Overview

Cirq is an open-source Python framework from Google Quantum AI for writing, manipulating, and running quantum circuits on today's noisy intermediate-scale quantum (NISQ) computers. Hardware details are a first-class concern, letting algorithm implementations target real devices. Circuits run on built-in simulators or on supported quantum hardware platforms.

Write quantum programs for today's hardware with hardware-aware abstractions.

Key Features

• Flexible gates: supports custom gate definitions and flexible circuit construction
• Symbolic parameters: circuits can use symbolic variables resolved at run time
• Circuit transformation: tools for transforming, compiling, and optimizing circuits
• Device modeling: models the topology and constraints of target devices
• Noise modeling: built-in noise models for simulating noisy circuits
• Multiple simulators: several quantum circuit simulators included
• High-performance simulation: integrates with qsim for large-scale fast simulation
• Ecosystem interop: works seamlessly with NumPy and SciPy

Target Audience

• Quantum algorithm researchers
• Quantum software developers
• Teachers and students in quantum computing courses
• Engineers working on quantum error correction
• Research teams needing NISQ hardware-aware programming

Quick Start

1. Install Cirq

pip install cirq

2. Run your first quantum circuit simulation

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)

3. Follow the official tutorials to keep learning circuit building and simulation

Core Advantages

• Hardware aware: abstractions designed for NISQ devices and real hardware constraints
• Strong performance: combines with qsim and related tools for high-performance simulation
• Rich ecosystem: integrates with Qualtran, OpenFermion, TensorFlow Quantum, and more
• Complete docs: official tutorials, videos, and examples from beginner to advanced

System Requirements

Notes

• Version requirement: only Python 3.11 and later are supported
• Hardware access: running on real quantum hardware requires accounts and authorization on the corresponding platform
• Examples: the examples directory in the repo covers textbook algorithms through advanced methods