alterlab-pennylane - SKILL.md Agent Skill

name: alterlab-pennylane description: Trains and differentiates quantum circuits with PennyLane, a hardware-agnostic quantum ML framework with automatic differentiation and device portability across IBM, Google, Rigetti, and IonQ. Use when training quantum circuits via gradients, building hybrid quantum-classical models, running variational algorithms (VQE, QAOA), building quantum neural networks, or integrating with PyTorch, JAX, or TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip. Part of the AlterLab Academic Skills suite. license: Apache-2.0 allowed-tools: Read Write Edit Bash(python:*) compatibility: No API key required for local simulation. Runs via uv run python; requires the pennylane Python package. Remote hardware (IBM, IonQ, Rigetti) needs separate provider credentials. metadata: skill-author: AlterLab version: "1.0.0"

PennyLane

Overview

PennyLane is a quantum computing library that enables training quantum computers like neural networks. It provides automatic differentiation of quantum circuits, device-independent programming, and seamless integration with classical machine learning frameworks.

Installation

Install using uv:

uv pip install pennylane

For quantum hardware access, install device plugins:

# 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

Quick Start

Build a quantum circuit and optimize its parameters:

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)

Core Capabilities

New to PennyLane? See references/getting_started.md for installation, QNodes, devices, gradients, and a first optimization loop.

1. Quantum Circuit Construction

Build circuits with gates, measurements, and state preparation. See references/quantum_circuits.md for:

Single and multi-qubit gates
Controlled operations and conditional logic
Mid-circuit measurements and adaptive circuits
Various measurement types (expectation, probability, samples)
Circuit inspection and debugging

2. Quantum Machine Learning

Create hybrid quantum-classical models. See references/quantum_ml.md for:

Integration with PyTorch, JAX, TensorFlow
Quantum neural networks and variational classifiers
Data encoding strategies (angle, amplitude, basis, IQP)
Training hybrid models with backpropagation
Transfer learning with quantum circuits

3. Quantum Chemistry

Simulate molecules and compute ground state energies. See references/quantum_chemistry.md for:

Molecular Hamiltonian generation
Variational Quantum Eigensolver (VQE)
UCCSD ansatz for chemistry
Geometry optimization and dissociation curves
Molecular property calculations

4. Device Management

Execute on simulators or quantum hardware. See references/devices_backends.md for:

Built-in simulators (default.qubit, lightning.qubit, default.mixed)
Hardware plugins (IBM, Amazon Braket, Google, Rigetti, IonQ)
Device selection and configuration
Performance optimization and caching
GPU acceleration and JIT compilation

5. Optimization

Train quantum circuits with various optimizers. See references/optimization.md for:

Built-in optimizers (Adam, gradient descent, momentum, RMSProp)
Gradient computation methods (backprop, parameter-shift, adjoint)
Variational algorithms (VQE, QAOA)
Training strategies (learning rate schedules, mini-batches)
Handling barren plateaus and local minima

6. Advanced Features

Leverage templates, transforms, and compilation. See references/advanced_features.md for:

Circuit templates and layers
Transforms and circuit optimization
Pulse-level programming
Catalyst JIT compilation
Noise models and error mitigation
Resource estimation

Common Workflows

Train a Variational Classifier

# 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)

Run VQE for Molecular Ground State

from pennylane import qchem

# 1. Build Hamiltonian (returns the qubit Hamiltonian and qubit count)
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. Set up UCCSD ansatz from the excitations
hf_state = qchem.hf_state(electrons=2, orbitals=n_qubits)
singles, doubles = qchem.excitations(electrons=2, orbitals=n_qubits)
s_wires, d_wires = qchem.excitations_to_wires(singles, doubles)

dev = qml.device('default.qubit', wires=n_qubits)

@qml.qnode(dev)
def vqe_circuit(params):
    qml.UCCSD(params, wires=range(n_qubits),
              s_wires=s_wires, d_wires=d_wires, init_state=hf_state)
    return qml.expval(H)

# 3. Optimize (one parameter per excitation)
opt = qml.AdamOptimizer(stepsize=0.1)
params = np.zeros(len(singles) + len(doubles), 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")

Switch Between Devices

# Define the circuit body once, bind it to a device on demand.
def circuit_body(params):
    qml.AngleEmbedding(params, wires=range(4))
    return qml.expval(qml.PauliZ(0))

def make_qnode(dev):
    return qml.qnode(dev)(circuit_body)

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

# Run on quantum hardware (IBM, via Qiskit Runtime)
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService(channel='ibm_quantum_platform')  # requires saved IBM Cloud credentials
backend = service.least_busy(operational=True, simulator=False)
dev_hw = qml.device('qiskit.remote', wires=4, backend=backend)
# On hardware, build the QNode with diff_method='parameter-shift' for gradients.
result_hw = make_qnode(dev_hw)(params)

Best Practices

Start with simulators - Test on default.qubit before deploying to hardware
Use parameter-shift for hardware - Backpropagation only works on simulators
Choose appropriate encodings - Match data encoding to problem structure
Initialize carefully - Use small random values to avoid barren plateaus
Monitor gradients - Check for vanishing gradients in deep circuits
Cache devices - Reuse device objects to reduce initialization overhead
Profile circuits - Use qml.specs() to analyze circuit complexity
Test locally - Validate on simulators before submitting to hardware
Use templates - Leverage built-in templates for common circuit patterns
Compile when possible - Use Catalyst JIT for performance-critical code

Resources

Official documentation: https://docs.pennylane.ai
Codebook (tutorials): https://pennylane.ai/codebook
QML demonstrations: https://pennylane.ai/qml/demonstrations
Community forum: https://discuss.pennylane.ai
GitHub: https://github.com/PennyLaneAI/pennylane