cirq

cirq is a skill for Claude Code from agentlas-ai/agentlas-desktop. It costs 67 tokens per session (3,049 once invoked), scanned A, a copy of cirq, Apache-2.0.

A Python framework for designing, simulating, and running quantum circuits, with particular support for Google Quantum AI hardware and noise-aware experiments.

In plain words
What is it for?
It helps build and optimize circuits, compile them for hardware gates, run parameter sweeps, model noise, and perform quantum characterization experiments.
Why use it?
It provides one workflow for testing circuits in simulation, modeling hardware noise, and targeting supported quantum processors.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit It helps build and optimize circuits, compile them for hardware gates, run parameter sweeps, model noise, and perform quantum characterization experiments.

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Install with agentmods
npx agentmods add skills/agentlas-ai/agentlas-desktop/cirq
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add agentlas-ai/agentlas-desktop --skill cirq
Clone the repo
git clone --depth 1 https://github.com/agentlas-ai/agentlas-desktop

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for cirq

README.md
[![agentmods](https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/cirq/github.svg)](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cirq)
Your own site
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cirq"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/cirq/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for cirq

Your own site · 80×15
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/cirq"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/cirq.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,049 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00067 $0.03049
Opus 5.5 $0.00027 $0.01220
Sonnet 5.5 $0.00013 $0.00610
Haiku 4.5 $0.00007 $0.00305

Measured 1mo ago against content hash 73b8e6e5da2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cirq scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to cirq — 98 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agentlas-science-skills/skills/cirq/SKILL.md · 387 lines

How it starts

The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

uv pip install "cirq==1.6.1"

For hardware integration (pin matching versions for reproducibility):

# 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

import cirq
import numpy as np

# Create qubits
q0, q1 = cirq.LineQubit.range(2)

# Build circuit
circuit = cirq.Circuit(
    cirq.H(q0),              # Hadamard on q0
    cirq.CNOT(q0, q1),       # CNOT with q0 control, q1 target
    cirq.measure(q0, q1, key='result')
)

print(circuit)

# Simulate
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=1000)

# Display results
print(result.histogram(key='result'))

Parameterized Circuit

import sympy

# Define symbolic parameter
theta = sympy.Symbol('theta')

# Create parameterized circuit
circuit = cirq.Circuit(
    cirq.ry(theta)(q0),
    cirq.measure(q0, key='m')
)

# Sweep over parameter values
sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)

# Process results
for params, result in zip(sweep, results):
    theta_val = params['theta']
    counts = result.histogram(key='m')
    print(f"θ={theta_val:.2f}: {counts}")

Read the full file on GitHub · 387 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 387 lines · 67 tokens per session scan A 73b8e6e5da2b

Subscribe to this mod's changes

cirq is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 67 tokens to every session and 3,049 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to cirq, differing in 98 lines, and is treated as a copy.

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