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-lev…
---
name: cirq
description: 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.
license: Apache-2.0 license
allowed-tools: Read Write Edit Bash
metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
---
# 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.… load the full skill through Skill Me