PennyLane

When to use

Use PennyLane to optimize parameterized circuits, build hybrid quantum-classical models, estimate molecular energies, or compare a validated circuit across devices. This skill targets stable PennyLane 0.45.1. The local examples use small synthetic systems; successful optimization does not establish quantum advantage, molecular accuracy, or hardware fidelity.

Installation

Create a dedicated environment; provider plugins and compiler dependencies should be resolved separately from unrelated scientific packages:

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

PennyLane 0.45 requires NumPy 2+. Core installation includes Lightning. For ML, use the tested JAX 0.7.1/JAXlib 0.7.1 pair or PyTorch; do not assume the latest JAX is supported by this PennyLane release. See device setup for optional plugin versions and their verification limits.

Workflow

1. Fix the problem convention: feature ordering and label encoding for ML; Hamiltonian sign for optimization; units, charge, multiplicity, basis, active space and mapping for chemistry.
2. Build a small analytic default.qubit circuit. Keep the quantum function separate from the QNode when reusing it on another backend.
3. Check a known value and a gradient against an analytic or finite-difference result before training. Use trainable pennylane.numpy arrays for Autograd, framework-native tensors for Torch/JAX.
4. Optimize while recording objective, gradient norm, seeds, ansatz shape and package versions. step_and_cost returns the cost before its update; evaluate the objective again for final reporting.
5. Validate independently: held-out examples and classical baselines for ML; particle number and a sector-appropriate classical reference for VQE; enumerated small instances for QAOA.
6. Introduce finite shots/noise, report uncertainty, inspect decomposed resources, then select a current accessible hardware backend and execution budget. Plugin portability does not guarantee identical gates, measurements or gradients.

Quick start: value, gradient and optimization

This self-contained example is executed by the skill's tests.

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}")

Load the relevant reference

• Getting started: QNodes, shots, random streams, trainability, batched parameters and simulator selection.
• Quantum circuits: controls, measurement feedback, wire ordering, QFT, transforms and current resource access.
• Quantum ML: executed TorchLayer and JAX training, stable classifier loss, feature scaling and evaluation.
• Quantum chemistry: H2 UCCSD, units, particle sector checks, dipoles, active spaces, geometry and excited-state caveats.
• Devices: simulators, provider contracts, credentials, current target discovery and source-only integration boundaries.
• Optimization: gradient checks, SPSA, QNG, MaxCut sign and exact QUBO-to-Ising conversion.
• Advanced features: templates, noise, parametrized Hamiltonian evolution, Catalyst and error-correction examples.

Failure checks

• Samples/counts require finite shots; states require a supporting simulator. Use qml.set_shots on the QNode rather than mutating device shots.
• Mid-circuit measurement values are symbolic. Use qml.cond, not Python if m.
• qml.specs in 0.45.1 returns CircuitSpecs; access .resources, not obsolete top-level dictionary keys. Record the transform level and account for split tapes.
• qml.qaoa.maxcut produces negative cut size. Minimize it directly.

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