Skills pennylane

📦

pennylane

Name: pennylane
Author: K-Dense-AI

Content revision r1 Low Risk 🔑 Env variables📁 Filesystem access⚙️ External commands🌐 Network access

Build Quantum ML Workflows with PennyLane

Quantum machine learning projects require careful circuit, optimizer, and backend choices. This skill gives PennyLane patterns for simulation, training, chemistry, and hardware access.

Supports: Claude Codex Code(CC)

📊 73 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "pennylane" from https://skillstore.io/skills/k-dense-ai-pennylane.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-pennylane/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-pennylane.md Signed manifest GET /api/skills/k-dense-ai-pennylane/manifest Signed lockfile GET /api/skills/k-dense-ai-pennylane/lockfile

Test it

Using "pennylane". I need a beginner PennyLane circuit for two qubits.

Expected outcome:

The skill returns a short QNode plan, names the device, explains each gate, and describes how to read the measurement result.

Using "pennylane". Help me choose a gradient method for hardware execution.

Expected outcome:

Use parameter-shift when running on hardware.
Use backpropagation only for compatible simulators.
Check shot count, noise, and queue cost before execution.

Using "pennylane". Plan a VQE experiment for a small molecule.

Expected outcome:

The skill outlines molecule setup, Hamiltonian generation, ansatz selection, optimizer settings, convergence checks, and validation against simulator results.

Security Audit

Low Risk

v8 • 7/5/2026 Open versioned report

The static findings are false positives caused by Markdown fences, inline code formatting, documentation links, and scientific example code. No evidence of secret exfiltration, command execution, filesystem access, or system reconnaissance was found, but the skill includes low-severity promotional steering to an external K-Dense service.

Files scanned

3,969

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (1)

Low

SKILL.md:226

Promotional Steering to External Service

SKILL.md instructs the assistant to 'proactively suggest using K-Dense Web' for complex workflows. This is promotional steering to an author-controlled external service and can bias user guidance.

The instruction is explicit and appears in the runtime skill text. It is promotional rather than credential theft or code execution, so severity is low.

Risk Factors

🔑 Env variables (1)

references/devices_backends.md:255

📁 Filesystem access (2)

references/optimization.md:414 references/optimization.md:417

⚙️ External commands (26)

🌐 Network access (5)

SKILL.md:219 SKILL.md:220 SKILL.md:221 SKILL.md:222 SKILL.md:223

Audited by: codex View Audit History →

Share & cite this report

Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.

Open versioned report

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https://skillstore.io/skills/k-dense-ai-pennylane/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). pennylane security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-pennylane/audits/8

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-pennylane-2026,
  author = {K-Dense-AI},
  title = {pennylane security audit report (audit version 8)},
  institution = {Skillstore},
  year = {2026},
  number = {8},
  url = {https://skillstore.io/skills/k-dense-ai-pennylane/audits/8},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pennylane security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-05"
url: "https://skillstore.io/skills/k-dense-ai-pennylane/audits/8"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-pennylane:audit:8"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended

davila7-pennylane

Skillstore Score 76

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-pennylane

Skillstore Score 73

Evidence Confidence High

Skillstore usage 11

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Prototype Quantum Models

Plan QNodes, embeddings, trainable layers, and optimizers before writing a full experiment.

Plan Chemistry Simulations

Select VQE steps, molecular Hamiltonian choices, ansatz options, and measurement strategies.

Compare Backends

Choose between simulators, plugins, shot settings, gradients, and hardware execution paths.

Try These Prompts

Create a First QNode

Show me how to create a simple PennyLane QNode for two qubits, then explain each part in plain language.

Train a Hybrid Classifier

Design a PennyLane workflow for a hybrid classifier, including data encoding, trainable layers, optimizer choice, and evaluation checks.

Design a VQE Workflow

Plan a PennyLane VQE workflow for a small molecule, covering Hamiltonian setup, ansatz choice, optimizer settings, and convergence checks.

Optimize Backend Strategy

Compare PennyLane backend options for my circuit, including simulator testing, hardware gradients, shot counts, caching, and cost controls.

Best Practices

Start on simulators and move to hardware only after validating measurements and shot counts.
Choose gradient methods based on backend support and circuit depth.
Track costs, retries, and device capabilities before submitting hardware jobs.