Cirq Repo Skill

Use this skill when a task is about Cirq, the Python framework for quantum circuits and near-term quantum computing workflows. It covers the Cirq package family: cirq/cirq-core, cirq-google, cirq-ionq, cirq-aqt, cirq-pasqal, and cirq-web.

First checks

• For package layout, Python support, optional dependencies, and route selection, read references/package-overview.md.
• For install/import, provider credential, serialization, or environment issues, read references/troubleshooting.md.
• For source freshness, read references/repo-provenance.md before relying on this skill for a different checkout or package version.
• To verify an active Python environment without credentials or network calls, run scripts/check_cirq_environment.py with --help or --text.

Install stance

For ordinary package use, prefer public packages:

python -m pip install cirq          # full Cirq family
python -m pip install cirq-core     # core circuits/simulation only
python -m pip install cirq-google   # add Google Quantum AI package

Cirq requires Python 3.11+. Local circuit construction and CPU simulation do not require CUDA, ROCm, MPS, TPU, cloud credentials, or live quantum hardware. Provider packages can be imported offline, but live service execution requires provider credentials and account/project/target access.

Route by task

User task	Read next
Build circuits, choose qubit classes, work with gates/operations/moments, debug measurement keys, parameters, diagrams, custom gates, JSON/QASM basics	core-circuits-and-ops
Run local simulations, sample measurements, sweep parameters, inspect results/histograms/state vectors/density matrices, add noise/channels	simulation-study-and-noise
Optimize, decompose, transform, route, or compile circuits to a gateset, topology, or provider constraint	transformers-and-compilation
Implement or validate algorithm examples, QFT/phase estimation/Grover/QAOA-like circuits, Pauli observables, expectation values	algorithms-and-observables
Use cirq_google, IonQ, AQT, Pasqal, cirq_web, provider serializers, credentials, offline provider validation, or JSON custom resolvers	hardware-providers-and-serialization

Operating guidance

1. Start with the narrowest sub-skill that matches the user intent. Avoid answering provider, simulator, transformer, or algorithm questions only from the root router.
2. Prefer verified public APIs and bundled examples over memory. The sub-skill references include inspected signatures, workflow recipes, and failure modes.
3. Use bundled scripts only for safe local checks. They do not contact cloud services, download data, or require the original repository checkout.
4. For live provider execution, stop and verify credentials, project/account, target/processor, queue/service availability, and provider-specific supported gates before calling remote APIs.
5. If a circuit fails provider serialization or device validation, route first through transformers-and-compilation to decompose, route, or target-gateset optimize the circuit.
6. If a sampled or simulated result looks wrong, check measurement key order, qubit order, unresolved parameters, random seeds, simulator choice, and noise model assumptions before changing algorithm logic.

Bundled root assets

• references/package-overview.md — package family, dependency stance, and top-level route map.
• references/troubleshooting.md — cross-cutting install/import, optional dependency, provider credential, and serialization recovery steps.
• references/repo-provenance.md — source commit, version, and evidence baseline for staleness checks.
• references/repo-routing-metadata.json — structured scenario metadata for managed repo-skill import.
• scripts/check_cirq_environment.py — local import/version/tiny simulation/JSON roundtrip diagnostic.