Agent skill · research science · aiskillstore
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 with autodiff use pennylane; for physics simulations use qutip.
Why this skill is useful
Provides domain-specific knowledge for designing and simulating quantum circuits that the AI wouldn't reliably generate on its own.
What it needs
Requires azure-quantum, cirq, cirq-aqt, cirq-google, cirq-ionq, cirq-pasqal installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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. Installation For hardware integration: Quick Start Basic Circuit Parameterized Circuit Core Capabilities Circuit Building For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see: references/building.md - Complete guide to circuit construction Common topics: Qubit types (GridQubit, LineQubit, NamedQubit) Single and two-qubit gates Parameterized gates and operations Custom gate decomposition Circuit organization with moments Standard circuit patterns (Bell states, GHZ, QFT) Import/export (OpenQASM, JSON) Working with qudits and observables Simulation For detailed information about simulating quantum circuits, including exact simulation, noisy simulation, parameter sweeps, and the Quantum Virtual Machine, see: references/simulation.md - Complete guide to quantum simulation Common topics: Exact simulation (state vector, density matrix) Sampling and measurements Parameter sweeps (single and multiple parameters) Noisy simulation State histograms and visualization Quantum Virtual Machine (QVM) Expectation values and observables Performance optimization Circuit Transformation For information about optimizing, compiling, and manipulating quantum circuits, see: references/transformation.md - Complete guide to circuit transformations Common topics: Transformer framework Gate decomposition Circuit optimization (merge gates, eject Z gates, drop negligible operations) Circuit compilation for hardware Qubit routing and SWAP insertion Custom transformers Transformation pipelines Hardware Integration For information about running circuits on real quantum hardware from various providers, see: references/hardware.md - Complete guide to hardware integration Supported providers: Google Quantum AI (cirq-google) - Sycamore, Weber …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/cirq--de3d81