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.
8.3
Rating
0
Installs
Machine Learning
Category
Exceptional quantum computing skill with comprehensive coverage of Cirq's capabilities. The description clearly identifies use cases (Google hardware, noise-aware circuits, characterization experiments) and differentiates from alternatives (qiskit, pennylane, qutip). SKILL.md provides excellent quick-start examples and organizes deep technical content into six well-structured reference files covering building, simulation, transformation, hardware, noise, and experiments. Task knowledge is outstanding with complete code templates for variational algorithms, hardware execution, and noise studies. Structure is exemplary - concise main file with detailed references. Novelty is strong: quantum circuit design, noise modeling, and hardware integration require specialized domain knowledge that would consume significant tokens for a CLI agent to navigate independently. The skill meaningfully reduces complexity for quantum computing workflows. Minor improvement: could add a troubleshooting decision tree, but existing 'Common Issues' section is already quite good.
K-Dense-AI
Skill Author
Loading SKILL.md…
