---

name: applications-qchem-resource description: "Use PennyLane application modules for qchem, mappings, QAOA, kernels, qcut, shadows, pulse, resources, and estimator workflows." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Application modules, qchem, and resources

Use this sub-skill when a task moves beyond a basic circuit into PennyLane application modules: quantum chemistry, fermionic/bosonic/spin mappings, QAOA, kernels, circuit cutting, shadows, pulse/FTQC notes, resource specs, or the estimator.

Read first

• references/api-reference.md: module map and verified signatures for qchem/resource/estimator examples.
• references/workflows.md: tiny qchem, resource, qcut/kernel, and mapping workflows.
• references/troubleshooting.md: optional dependency, dataset, backend, and scale pitfalls for application modules.
• scripts/resource_and_qchem_smoke.py: safe CPU smoke for resource specs and a minimal qchem object.

Route within this sub-skill

• Quantum chemistry: qp.qchem.Molecule, qp.qchem.molecular_hamiltonian, tapering, symmetry generators, OpenFermion conversions, basis/structure utilities.
• Operator mappings: qp.FermiWord, qp.FermiSentence, qp.jordan_wigner, qp.parity_transform, qp.bravyi_kitaev, qp.BoseWord, qp.BoseSentence, qp.binary_mapping, qp.unary_mapping, qp.christiansen_mapping, and spin utilities.
• Algorithms: qp.qaoa, qp.kernels, qp.qcut, qp.shadows, qp.pulse, qp.ftqc, and qp.liealg where evidence and optional dependencies support the request.
• Resource estimation: qp.specs(qnode) for circuit specs, qp.resource for resource-level objects, and qp.estimator.estimate for estimator workflows.

Boundaries

• Use ../operators-transforms/SKILL.md for low-level operator arithmetic, matrices, transforms, and custom operators.
• Use ../circuits-devices/SKILL.md for QNode/device/measurement execution details.
• Use ../gradients-interfaces/SKILL.md for differentiating variational algorithms.
• Use ../io-data-logging/SKILL.md for importing/exporting circuits or remote datasets.

Minimal patterns

Resource specs:

import pennylane as qp

dev = qp.device("default.qubit", wires=2)

@qp.qnode(dev)
def circuit(theta):
    qp.RX(theta, 0)
    qp.CNOT([0, 1])
    return qp.expval(qp.Z(1))

print(qp.specs(circuit)(0.2))

Tiny qchem object without external solver work:

symbols = ["H", "H"]
coordinates = qp.numpy.array([[0.0, 0.0, -0.35], [0.0, 0.0, 0.35]])
mol = qp.qchem.Molecule(symbols, coordinates, unit="angstrom")
print(mol.n_electrons)

Verification cues

State optional dependency requirements up front. Many application workflows are CPU-capable in small cases but may need external chemistry solvers, CVX/KAHYPAR/opt_einsum, OpenQASM/Qualtran/Qiskit integrations, or accelerator plugins for scale. Use tiny local examples before promising a full scientific workflow.