Pennylane

What it does

When your agent uses it

• Variational quantum algorithms
• Quantum machine learning
• Simulator validation
• Moving validated circuits to provider plugins

Example prompts

• “Use the pennylane skill to build and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows”
• “/pennylane”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.11+ and PennyLane 0.45.1 with NumPy 2+. Optional PyTorch, JAX or provider plugins need separate compatible environments. Local simulation needs no credentials; hardware requires provider credentials and network access.
• Pre-approved tools (allowed-tools): Read, Bash, Python

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Fix the problem convention: feature ordering and label encoding for ML;
2. 2Build a small analytic default.qubit circuit. Keep the quantum function
3. 3Check a known value and a gradient against an analytic or finite-difference
4. 4Optimize while recording objective, gradient norm, seeds, ansatz shape and
5. 5Validate independently: held-out examples and classical baselines for ML;
6. 6Introduce finite shots/noise, report uncertainty, inspect decomposed resources,

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Bash
  • Python

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • docs.pennylane.ai
  • arxiv.org
  • github.com
  • doi.org
  • export.arxiv.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.11+ and PennyLane 0.45.1 with NumPy 2+. Optional PyTorch, JAX or provider plugins need separate compatible environments. Local simulation needs no credentials; hardware requires provider credentials and network access.

  From compatibility in the SKILL.md frontmatter.

Context cost

Pennylane loads about 1.8k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 664 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~89

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Bash, Python

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its Apache-2.0 licence (© K-Dense-AI). 664 words, ~1,805 tokens.

name

pennylane

description

Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for open-system dynamics use qutip.

allowed-tools

Read, Bash, Python

compatibility

Requires Python 3.11+ and PennyLane 0.45.1 with NumPy 2+. Optional PyTorch, JAX or provider plugins need separate compatible environments. Local simulation needs no credentials; hardware requires provider credentials and network access.

license

Apache-2.0 license

metadata.version

2.0

metadata.skill-author

K-Dense Inc.

metadata.last-reviewed

2026-10-01

metadata.upstream-version

0.45.1

uv venv --python 3.13 .venv-pennylane
uv pip install --python .venv-pennylane/bin/python "pennylane==0.45.1"

import pennylane as qml
from pennylane import numpy as np

dev = qml.device("default.qubit", wires=1)

@qml.qnode(dev, interface="autograd", diff_method="backprop")
def energy(theta):
    qml.RY(theta, wires=0)
    return qml.expval(qml.Z(0))

theta = np.array(0.3, requires_grad=True)
assert np.allclose(energy(theta), np.cos(theta))
assert np.allclose(qml.grad(energy)(theta), -np.sin(theta))
opt = qml.GradientDescentOptimizer(stepsize=0.2)
for _ in range(80):
    theta = opt.step(energy, theta)
final_energy = float(energy(theta))
assert final_energy < -0.999
print(f"[OK] final expectation = {final_energy:.6f}")

© K-Dense-AI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in skills/pennylane of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/advanced_features.md
• references/devices_backends.md
• references/getting_started.md
• references/optimization.md
• references/quantum_chemistry.md
• references/quantum_circuits.md
• references/quantum_ml.md

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/pennylane