Qiskit
技能 已验证 活跃IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
To enable users to effectively leverage the Qiskit quantum computing framework for building, optimizing, and executing quantum circuits, particularly when targeting IBM Quantum hardware.
功能
- Detailed Qiskit installation and setup guides
- Comprehensive circuit building capabilities
- Execution on simulators and IBM Quantum hardware
- Circuit optimization and transpilation
- Visualization of circuits, results, and states
使用场景
- Targeting IBM Quantum hardware for quantum computations
- Working with Qiskit Runtime for production workloads
- Needing IBM optimization tools for quantum circuits
- Learning and implementing quantum algorithms with Qiskit
非目标
- Providing alternatives to IBM Quantum hardware
- Covering quantum computing frameworks other than Qiskit
- Deep dives into theoretical quantum physics beyond Qiskit applications
工作流
- Install Qiskit and set up IBM Quantum account (references/setup.md)
- Build quantum circuits using gates and operations (references/circuits.md)
- Optimize circuits for hardware via transpilation (references/transpilation.md)
- Execute circuits using Sampler or Estimator primitives (references/primitives.md)
- Analyze and visualize results (references/visualization.md)
实践
- Quantum circuit design
- Hardware-specific optimization
- Hybrid quantum-classical algorithms
- Best practices for execution and analysis
先决条件
- Python 3.8+ (or recommended version)
- uv package manager
- Qiskit and visualization dependencies
- IBM Quantum account and API token (for hardware execution)
Execution
- info:Pinned dependenciesThe skill instructs users to install Qiskit via `uv pip`, which handles dependency management. While explicit pinning isn't shown in the SKILL.md, `uv` typically manages dependencies effectively. The use of `uv pip install qiskit` implies latest version installation, not pinned.
安装
npx skills add K-Dense-AI/claude-scientific-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
Cirq
98Google 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.
Cirq Quantum Computing with Python
98Part of the AlterLab Academic Skills suite. 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.
Alterlab Qiskit
95Part of the AlterLab Academic Skills suite. IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
Qutip
99Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
Embedding Strategies
100Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Aws Cdk Development
100AWS Cloud Development Kit (CDK) 专家，用于使用 TypeScript/Python 构建云基础设施。在创建 CDK 堆栈、定义 CDK 构造、实现基础设施即代码，或当用户提及 CDK、CloudFormation、IaC、cdk synth、cdk deploy，或希望以编程方式定义 AWS 基础设施时使用。涵盖 CDK 应用结构、构造模式、堆栈组合和部署工作流。