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cirqlisted

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.
tassiovale/claude-code-kit · ★ 10 · AI & Automation · score 75
Install: claude install-skill tassiovale/claude-code-kit
# 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 impo