quantum-computing
Designs and analyzes quantum computing solutions including quantum circuit construction, algorithm implementation, error correction, and quantum advantage assessment; trigger when users discuss qubits, quantum gates, quantum algorithms, or quantum hardware.
Best use case
quantum-computing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Designs and analyzes quantum computing solutions including quantum circuit construction, algorithm implementation, error correction, and quantum advantage assessment; trigger when users discuss qubits, quantum gates, quantum algorithms, or quantum hardware.
Teams using quantum-computing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/quantum-computing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How quantum-computing Compares
| Feature / Agent | quantum-computing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Designs and analyzes quantum computing solutions including quantum circuit construction, algorithm implementation, error correction, and quantum advantage assessment; trigger when users discuss qubits, quantum gates, quantum algorithms, or quantum hardware.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## When to Trigger Activate this skill when the user mentions: - Quantum circuits, quantum gates (Hadamard, CNOT, Toffoli) - Qubits, superposition, entanglement, measurement - Quantum algorithms (Shor's, Grover's, VQE, QAOA) - Quantum error correction, decoherence, noise models - Quantum advantage, quantum supremacy, complexity classes (BQP) - Quantum hardware (superconducting, trapped ion, photonic) - Quantum simulation, quantum chemistry applications ## Step-by-Step Methodology 1. **Problem formulation** - Determine if the problem has a known quantum advantage. Map the problem to a quantum computing framework: gate-based, adiabatic, or measurement-based. Identify required qubit count and circuit depth. 2. **Algorithm selection** - For search: Grover's (quadratic speedup). For factoring: Shor's. For optimization: QAOA or quantum annealing. For chemistry: VQE or QPE. For machine learning: quantum kernels or variational classifiers. 3. **Circuit design** - Construct the quantum circuit using elementary gates (H, CNOT, Rz, Ry). Decompose multi-qubit gates into native gate sets. Minimize circuit depth and CNOT count for near-term hardware compatibility. 4. **Simulation** - Simulate circuit on classical hardware using Qiskit Aer, Cirq, or PennyLane. For small systems (<30 qubits), use statevector simulation. For larger systems, use tensor network or MPS methods. 5. **Noise analysis** - Model realistic noise: single-qubit and two-qubit gate errors, measurement errors, T1/T2 decoherence times. Use noise models from real hardware backends (IBM Quantum, IonQ). 6. **Error mitigation / correction** - For near-term (NISQ): zero-noise extrapolation, probabilistic error cancellation, dynamical decoupling. For fault-tolerant: surface codes, repetition codes, logical qubit encoding. 7. **Results analysis** - Compare quantum vs. classical performance. Report circuit metrics (depth, gate count, qubit count). Assess scalability and resource requirements for practical problem sizes. ## Key Databases and Tools - **Qiskit (IBM)** - Quantum SDK with hardware access - **Cirq (Google)** - Quantum circuit framework - **PennyLane (Xanadu)** - Quantum ML framework - **Amazon Braket** - Cloud quantum computing - **Quantum Algorithm Zoo** - Catalog of quantum algorithms - **IBM Quantum / IonQ** - Real hardware backends ## Output Format - Quantum circuits in standard notation (Qiskit/OpenQASM or circuit diagrams). - State vectors or density matrices for small systems. - Measurement histograms with shot counts and error bars. - Resource estimates: qubit count, circuit depth, T-gate count, total gate count. ## Quality Checklist - [ ] Problem-quantum advantage mapping justified - [ ] Circuit decomposed into hardware-native gate set - [ ] Qubit count and circuit depth reported - [ ] Noise model specified if simulating realistic conditions - [ ] Classical baseline comparison provided - [ ] Error mitigation strategy appropriate for NISQ era - [ ] Measurement shot count sufficient for statistical significance - [ ] Scalability analysis for practical problem sizes included
Related Skills
xurl
A CLI tool for making authenticated requests to the X (Twitter) API. Use this skill when you need to post tweets, reply, quote, search, read posts, manage followers, send DMs, upload media, or interact with any X API v2 endpoint.
xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
writing
No description provided.
world-bank-data
World Bank Open Data API for development indicators. Use when: user asks about GDP, population, poverty, health, or education statistics by country. NOT for: real-time financial data or stock prices.
wikipedia-search
Search and fetch structured content from Wikipedia using the MediaWiki API for reliable, encyclopedic information
wikidata-knowledge
Query Wikidata for structured knowledge using SPARQL and entity search. Use when: (1) finding structured facts about entities (people, places, organizations), (2) querying relationships between entities, (3) cross-referencing external identifiers (Wikipedia, VIAF, GND, ORCID), (4) building knowledge graphs from linked data. NOT for: full-text article content (use Wikipedia API), scientific literature (use semantic-scholar), geospatial data (use OpenStreetMap).
weather
Get current weather and forecasts via wttr.in or Open-Meteo. Use when: user asks about weather, temperature, or forecasts for any location. NOT for: historical weather data, severe weather alerts, or detailed meteorological analysis. No API key needed.
wacli
Send WhatsApp messages to other people or search/sync WhatsApp history via the wacli CLI (not for normal user chats).
voice-call
Start voice calls via the OpenClaw voice-call plugin.
visualization
Create publication-quality scientific figures and plots using Python (matplotlib, seaborn, plotly). Supports bar charts, scatter plots, heatmaps, box plots, violin plots, survival curves, network graphs, and more. Use when user asks to plot data, create figures, make charts, visualize results, or generate publication-ready graphics. Triggers on "plot", "chart", "figure", "graph", "visualize", "heatmap", "scatter plot", "bar chart", "histogram".
video-frames
Extract frames or short clips from videos using ffmpeg.
venue-templates
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.