Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).
8.7
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Machine Learning
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Excellent quantum computing skill with comprehensive coverage of Cirq's capabilities. The description clearly articulates when to use the skill (quantum algorithms, circuit design, simulation, hardware execution, noise modeling, experiments). SKILL.md provides a well-structured overview with quick starts, common patterns, and best practices, while appropriately delegating detailed documentation to organized reference files. The skill offers substantial value by encapsulating complex quantum computing workflows (VQE, QAOA, hardware integration across multiple providers, noise characterization) that would be token-intensive and error-prone for a CLI agent to implement from scratch. Minor deduction on novelty as some basic circuit operations are straightforward, but the overall complexity of quantum computing, multi-provider hardware integration, and experimental frameworks justifies high novelty. The modular structure with six reference files keeps SKILL.md concise while ensuring comprehensive task knowledge is available.
davila7
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