Pennylane

What it does

When your agent uses it

• Working with quantum circuits
• Variational quantum algorithms (VQE
• Quantum neural networks
• Hybrid quantum-classical models

Example prompts

• “/pennylane”

Requirements

• Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

1. 1Quantum Circuit Construction
2. 2Quantum Machine Learning
3. 3Quantum Chemistry
4. 4Device Management
5. 5Optimization
6. 6Advanced Features

What it can do on your machine

Read from SKILL.md and the folder at commit c0ca7da. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  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):

  • pennylane.ai
  • docs.pennylane.ai
  • discuss.pennylane.ai
  • github.com

  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.

Context cost

Pennylane loads about 1.9k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 175 tokens; SKILL.md has 471 words of instructions outside code blocks.

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

~175

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

~1.9k

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

~24k

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

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 davila7/claude-code-templates at commit c0ca7da, republished under its MIT licence (© davila7). 471 words, ~1,893 tokens.

name

pennylane

description

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

uv pip install pennylane

# IBM Quantum
uv pip install pennylane-qiskit

# Amazon Braket
uv pip install amazon-braket-pennylane-plugin

# Google Cirq
uv pip install pennylane-cirq

# Rigetti Forest
uv pip install pennylane-rigetti

# IonQ
uv pip install pennylane-ionq

import pennylane as qml
from pennylane import numpy as np

# Create device
dev = qml.device('default.qubit', wires=2)

# Define quantum circuit
@qml.qnode(dev)
def circuit(params):
    qml.RX(params[0], wires=0)
    qml.RY(params[1], wires=1)
    qml.CNOT(wires=[0, 1])
    return qml.expval(qml.PauliZ(0))

# Optimize parameters
opt = qml.GradientDescentOptimizer(stepsize=0.1)
params = np.array([0.1, 0.2], requires_grad=True)

for i in range(100):
    params = opt.step(circuit, params)

# 1. Define ansatz
@qml.qnode(dev)
def classifier(x, weights):
    # Encode data
    qml.AngleEmbedding(x, wires=range(4))

    # Variational layers
    qml.StronglyEntanglingLayers(weights, wires=range(4))

    return qml.expval(qml.PauliZ(0))

# 2. Train
opt = qml.AdamOptimizer(stepsize=0.01)
weights = np.random.random((3, 4, 3))  # 3 layers, 4 wires

for epoch in range(100):
    for x, y in zip(X_train, y_train):
        weights = opt.step(lambda w: (classifier(x, w) - y)**2, weights)

from pennylane import qchem

# 1. Build Hamiltonian
symbols = ['H', 'H']
coords = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.74])
H, n_qubits = qchem.molecular_hamiltonian(symbols, coords)

# 2. Define ansatz
@qml.qnode(dev)
def vqe_circuit(params):
    qml.BasisState(qchem.hf_state(2, n_qubits), wires=range(n_qubits))
    qml.UCCSD(params, wires=range(n_qubits))
    return qml.expval(H)

# 3. Optimize
opt = qml.AdamOptimizer(stepsize=0.1)
params = np.zeros(10, requires_grad=True)

for i in range(100):
    params, energy = opt.step_and_cost(vqe_circuit, params)
    print(f"Step {i}: Energy = {energy:.6f} Ha")

# Same circuit, different backends
circuit_def = lambda dev: qml.qnode(dev)(circuit_function)

# Test on simulator
dev_sim = qml.device('default.qubit', wires=4)
result_sim = circuit_def(dev_sim)(params)

# Run on quantum hardware
dev_hw = qml.device('qiskit.ibmq', wires=4, backend='ibmq_manila')
result_hw = circuit_def(dev_hw)(params)

© davila7, MIT. 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 cli-tool/components/skills/scientific/pennylane of davila7/claude-code-templates.

• 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 c0ca7da

Used in 7 other repositories

We found 9 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 7 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

• Microck/ordinary-claude-skillsskills_all/claude-scientific-skills/scientific-skills/pennylaneskills_categorized/scientific-computing/claude-scientific-skills/scientific-skills/pennylane
• aiskillstore/marketplaceskills/davila7/pennylaneskills/k-dense-ai/pennylane
• lamm-mit/scienceclawskills/pennylane
• agent-skills-hub/agent-skills-hubskills/pennylane
• LeonChaoX/qinyan-academic-skillsskills/10-材料科学与物理计算/pennylane
• BioTender-max/awesome-bio-agent-skillsskills/kdense/pennylane
• infometa/workbuddyskillsplugins/codebuddy-plugins-official/external_plugins/scientific-skills/pennylane