Skill

/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

One skill from scientific-agent-skills.

shell

$ npx -y skills add K-Dense-AI/scientific-agent-skills --skill cirq --agent claude-code

Installs just this skill. Get the whole plugin for auto-invocation.

How it fires

How this skill gets triggered: by you, by Claude, or both.

Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/cirq

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

Stats

Stars31,545

Forks3,146

LanguagePython

LicenseMIT

Ships with scientific-agent-skills

SKILL.md

cirq.SKILL.md

---
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.