Organize quantum algorithm demos with YAML frontmatter and Markdown instructions.
npx skills add https://github.com/unitarylab/quantum-skills --skill algorithms-unitarylab
Please help me install this Agent Skill. Skill: algorithms Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms Command: npx skills add https://github.com/unitarylab/quantum-skills --skill algorithms-unitarylab
Curates and exposes a structured library of quantum skill units, enabling learners to discover, understand, and execute hands-on quantum algorithm demos built on UnitaryLab tooling.
Choose a sub-skill (for example, QPE or QAOA) from this library and run its One-Step Run Example Command to begin an end-to-end learning workflow.
High-intent search queries and answers about installing and using this skill.
You can discover and execute runnable quantum algorithm demos by selecting a sub-skill like QPE or QAOA from the library and running its One-Step Run Example Command for an end-to-end workflow.
Available quantum algorithm primitives span linear systems, cryptography, Hamiltonian simulation, and Schrödingerization, providing structured frontmatter-driven discovery for comparative learning across multiple backends.
Yes, the library provides progressively loaded content, circuit visualization, and run-ready examples across simulators, allowing you to start an end-to-end learning workflow directly by choosing a sub-skill.
The library identifies and organizes quantum skill units using YAML frontmatter with a name and description at the root of each skill, loading the Markdown body as activation instructions for structured pedagogy.
Yes, the library enforces structured discovery across multiple domains including Hamiltonian simulation and Schrödingerization demos, allowing users to quickly locate and execute these specific quantum algorithms.
YAML frontmatter with a name and description is enforced at the root of each quantum skill unit to enable structured discovery, allowing the system to load the Markdown body as activation instructions for execution.
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