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Cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

Science & Simulation|v1|Updated 7/2/2026|GitHub source
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# 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

Requires Python 3.11+. Current stable release: **1.6.1** (August 2025). Vendor packages share the same version number.

```bash
uv pip install "cirq==1.6.1"
```

For hardware integration (pin matching versions for reproducibility):
```bash
# Google Quantum Engine (requires approved GCP project access)
uv pip install "cirq-google==1.6.1"

# IonQ
uv pip install "cirq-ionq==1.6.1"

# AQT (Alpine Quantum Technologies)
uv pip install "cirq-aqt==1.6.1"

# Pasqal
uv pip install "cirq-pasqal==1.6.1"

# Azure Quantum (IonQ, Honeywell/Quantinuum backends)
uv pip install "azure-quantum[cirq]"
```

For latest features during development, omit version pins; for production or hardware runs, pin all packages to the same Cirq release.

## Quick Start

### Basic Circuit

```python
import cirq
import numpy as np

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Cirq - AgentArmory Skill — AgentArmory