Cirq - Quantum Computing with Python

Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.

When to Use This Skill

Use this skill when:

• Building, simulating, or optimizing NISQ circuits in Python
• Running jobs on Google Quantum AI processors (via cirq-google) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
• Modeling noise, compiling to hardware gatesets, or designing characterization experiments
• Using parameter sweeps, transformers, or the ReCirq experiment patterns

For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

Installation

Examples target Cirq 1.7.0 on Python 3.11+. Keep Cirq vendor packages on the matching release; independently versioned integrations such as Azure Quantum need their own compatibility check.

uv pip install "cirq-core==1.7.0"
# Optional OpenQASM import dependency
uv pip install ply

For hardware integration (pin matching versions for reproducibility):

# Google Quantum Engine (requires approved GCP project access)
uv pip install "cirq-google==1.7.0"

# IonQ
uv pip install "cirq-ionq==1.7.0"

# AQT (Alpine Quantum Technologies)
uv pip install "cirq-aqt==1.7.0"

# Pasqal
uv pip install "cirq-pasqal==1.7.0"

# Azure: use a SEPARATE environment; its current extras require Cirq <1.7.
uv pip install "qdk[azure,cirq]==1.32.3" "azure-quantum==3.13.0"

Pin all cirq-* packages in an environment to the same release. The Azure example targets its supported Cirq 1.6.1 stack separately; do not force Cirq 1.7.0 into it. Cirq 1.7.0 updates the IonQ adapter to API v0.4.

Local examples were checked with synthetic circuits. Hardware examples are illustrative and were checked against released source and provider documentation, without submitting cloud jobs. See review evidence.

Quick Start

Basic Circuit