kdense-pennylane

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.

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Repository: eightmm/codex-science
Last source activity: July 10, 2026 at 05:18
Detected SKILL.md language: English
Stars: 2
Forks: 0

Install options

The review-first prompt is selected by default. You can switch to a direct command or download a local copy.

Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.

A direct command skips the review prompt. Inspect the source before running it.

npx skills add https://github.com/eightmm/codex-science --skill kdense-pennylane

The command stays on one line. Scroll horizontally to inspect it before copying.

Prefer a local copy? Download the files currently available to SkillsMP.

Review the source files

Read SKILL.md and any companion files shown by SkillsMP before deciding whether to install.

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2 files

SKILL.md

Source instructions · Read-only preview

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Kdense Pennylane

Use this Codex adapter for the pinned upstream scientific skill.

Catalog status

Status: active

Source: kdense

Audit reasons: none

Upstream license: Apache-2.0 license

Inventory: ../../inventory.json

Upstream instructions: ../../../vendor/scientific-agent-skills/skills/pennylane/SKILL.md

Procedure

Resolve the plugin root as three directories above this skill directory.

Read the matching inventory record and verify its status, reasons, source path, and pinned revision.

Read the upstream instructions only after confirming this record is still active in the current inventory.

Treat upstream frontmatter as source metadata, not as Codex configuration. Follow the instruction body only where it is compatible with available Codex tools and the current user-approved plan.

Treat tools named for another agent runtime as capability requests. Map them to an available Codex tool only when the behavior is equivalent; otherwise report the unavailable capability.

Ask before credentials, package installation, new network hosts, remote compute, writes, paid services, destructive actions, or imported executable code.

Use $science-provenance for produced scientific outputs and $science-review before presenting scientific claims.

Boundaries

This wrapper does not grant credentials, network access, package installation, or script execution.

Do not change an inactive record to active from inside a research task.

Preserve failures, uncertainty, source identifiers, and licensing constraints.

name

kdense-pennylane

description