Skills pennylane

📦

pennylane

Name: pennylane
Author: davila7

Content revision r2 Safe 🔑 Env variables📁 Filesystem access⚙️ External commands🌐 Network access

Build Quantum Workflows with PennyLane

Quantum workflows require careful circuit, device, gradient, and framework choices. This skill provides PennyLane patterns for building, training, and evaluating those workflows.

Supports: Claude Codex Code(CC)

📊 76 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/davila7-pennylane.md and its manifest at https://skillstore.io/api/skills/davila7-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/davila7-pennylane.md Signed manifest GET /api/skills/davila7-pennylane/manifest Signed lockfile GET /api/skills/davila7-pennylane/lockfile

Test it

Using "pennylane". Create a basic entangled circuit and explain its result.

Expected outcome:

A two-qubit simulator setup with one Hadamard gate and one controlled operation.
Probability measurements and an explanation of correlated outcomes.
Checks for wire count, shot mode, and result shape.

Using "pennylane". Outline a variational classifier for four features.

Expected outcome:

An angle-encoding strategy and a layered trainable circuit.
Parameter shape guidance, optimizer selection, and loss evaluation.
Training checks for gradients, convergence, and held-out accuracy.

Using "pennylane". Plan a simulator-first VQE study for hydrogen.

Expected outcome:

A molecular Hamiltonian preparation sequence and ansatz recommendation.
A simulator validation plan with convergence and energy checks.
A hardware transition checklist covering shots, supported gates, errors, and cost.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 45 static alerts are false positives caused by Markdown formatting, ordinary terminology, Python list appends, placeholder credentials, and documentation links. The files contain instructional examples, with no embedded scripts, prompt injection, real secrets, dynamic command construction, or automatic network behavior. No remediation is required for the reported patterns.

Files scanned

3,964

Lines analyzed

Review items

False positives ignored

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:216 SKILL.md:217 SKILL.md:218 SKILL.md:219 SKILL.md:220

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

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

Copy report link

https://skillstore.io/skills/davila7-pennylane/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown badge

[![Skillstore security assessment](https://skillstore.io/badges/skills/davila7-pennylane/security.svg)](https://skillstore.io/skills/davila7-pennylane?utm_source=security_passport_badge)

HTML badge

<a href="https://skillstore.io/skills/davila7-pennylane?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-pennylane/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

Embed card

<iframe src="https://skillstore.io/embed/skills/davila7-pennylane.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). pennylane security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pennylane/audits/9

BibTeX citation

@techreport{davila7-davila7-pennylane-2026,
  author = {davila7},
  title = {pennylane security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-pennylane/audits/9},
  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 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pennylane/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-pennylane:audit:9"
    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 Current

davila7-pennylane

Skillstore Score 76

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

K-Dense-AI

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 variational circuits

Create QNodes, choose measurements, select gradients, and test optimization loops on local simulators.

Build hybrid learning models

Connect parameterized quantum circuits with PyTorch, JAX, or TensorFlow training workflows.

Explore molecular simulations

Prepare molecular Hamiltonians, design VQE workflows, and compare estimated ground-state energies.

Try These Prompts

Create a first QNode

Create a two-qubit PennyLane QNode on default.qubit. Apply RX, RY, and CNOT gates, then return one expectation value.

Train a variational classifier

Design a PennyLane training loop for a variational classifier. Explain parameter shapes, gradient method, optimizer choice, and simulator validation.

Integrate a quantum layer

Build a hybrid PennyLane and PyTorch model for four input features. Include batching, a quantum layer, loss selection, and training checks.

Plan hardware-aware VQE

Plan a VQE workflow for hydrogen that starts on a simulator and targets IBM hardware. Address ansatz, gradients, shots, cost, and errors.

Best Practices

Validate every workflow on a simulator before sending jobs to external hardware.
Use placeholder credentials in examples, then supply real secrets through provider-approved secure configuration.
Confirm device capabilities, gradient compatibility, shot settings, and estimated cost before execution.

Avoid

Do not paste real API tokens into prompts, source files, notebooks, or generated examples.
Do not assume simulator gradients, operations, or deterministic results transfer unchanged to hardware.
Do not submit expensive hardware jobs before testing circuit dimensions, shots, and provider limits.