---

name: operators-transforms description: "Use PennyLane operations, templates, observables, op math, decompositions, compile pipelines, transforms, and custom-operator validation." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Operators, templates, and transforms

Use this sub-skill when a task asks which operation/template to use, how to compose observables, how to inspect operator matrices/eigenvalues, how to decompose or compile a circuit, or how to add/validate custom operators.

Read first

• references/api-reference.md: verified signatures and API groups for operations, templates, op math, and transforms.
• references/workflows.md: recipes for operator composition, template shape checks, decomposition/compile, and custom operators.
• references/troubleshooting.md: shape, wire, differentiability, decomposition, and custom-operator failures.
• scripts/operator_validation.py: tiny custom operator plus assert_valid check.
• scripts/decompose_smoke.py: safe decomposition/compile smoke.

Route within this sub-skill

• Basic gates and observables: use top-level gates like qp.RX, qp.RY, qp.RZ, qp.CNOT, qp.Hadamard, qp.PauliX/Y/Z, and measurement observables through circuits/devices.
• Templates: use qp.AngleEmbedding, qp.AmplitudeEmbedding, qp.StronglyEntanglingLayers, state-preparation templates, subroutines, and tensor-network templates when a workflow needs reusable ansatz structure.
• Operator arithmetic: use qp.sum, qp.prod, qp.s_prod, qp.pow, qp.adjoint, qp.ctrl, qp.cond, qp.exp, qp.dot, qp.commutator, and qp.pauli_decompose to compose or analyze operators.
• Inspection: use qp.matrix, qp.eigvals, qp.generator, qp.is_unitary, qp.is_hermitian, qp.is_commuting, qp.equal, qp.assert_equal, qp.simplify, and qp.map_wires.
• Transforms: use qp.transform for custom tape transforms; use packaged transforms such as qp.compile, qp.decompose, batching transforms, qp.defer_measurements, dynamic-one-shot, Clifford+T decomposition, and pattern matching.
• Custom operators: route to the custom-operator workflow and validate with pennylane.ops.functions.assert_valid(op).

Boundaries

• Device, measurement execution, shots, and drawing are owned by ../circuits-devices/SKILL.md.
• Gradient transforms and differentiability choices are owned by ../gradients-interfaces/SKILL.md.
• Qchem/fermionic/bosonic/spin operator mappings are owned by ../applications-qchem-resource/SKILL.md, but the resulting qubit operators can be inspected here.
• Source-code lint/test/contribution policy is owned by ../repo-development/SKILL.md.

Minimal pattern

import pennylane as qp

op = 0.5 * qp.PauliZ(0) + qp.PauliX(1) @ qp.PauliZ(2)
print(qp.simplify(op))
print(qp.matrix(qp.RX(0.2, wires=0)))

@qp.qnode(qp.device("default.qubit", wires=3))
def circuit(x):
    qp.AngleEmbedding(x, wires=range(3), rotation="Y")
    qp.StronglyEntanglingLayers(qp.numpy.zeros((1, 3, 3)), wires=range(3))
    return qp.expval(op)

print(qp.draw(qp.compile(circuit))(qp.numpy.array([0.1, 0.2, 0.3])))

Verification cues

For a useful answer, include expected parameter shapes, wire conventions, transform level (user, device, or gradient) when drawing/compiling, and a validation step such as assert_valid, qp.matrix, qp.draw, or focused tests for source changes.