pennylane

pennylane is a Physics, mathematics & simulation skill for AI agents, published by K-Dense-AI in the claude-scientific-skills repository on GitHub. Skillspace rates pennylane C (74/100) and it passed the safety scan; a judge model has also reviewed it.

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch or JAX. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

Grade
C
Quality
74 / 100
Safety
Passed
GitHub stars
44,723

Placed #2 of 6 in the judged category final for Physics, mathematics & simulation.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubK-Dense-AI/claude-scientific-skills

Our review

Architecture
100 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Judge review

Placed #2 of 6 in the judged category final for Physics, mathematics & simulation.

  • Trigger9292
  • Instructions8888
  • Specificity9090
  • Safety9595
  • Disclosure9090
  • Maintenance8282
Comprehensive quantum-ML reference with pinned per-provider versions, runnable VQE and variational-classifier code, and clean disambiguation from qiskit, cirq, and qutip.

Not checked yet

  • Category is a classifier prediction; no model has read this skill yet
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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