---

name: keras-quantum-layers description: "Route TensorFlow Quantum Keras-layer tasks for circuit wiring, readout, and trainable hybrid models." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

keras-quantum-layers

Use this sub-skill when the user wants to build, call, or debug tfq.layers models.

Owns

• AddCircuit append/prepend wiring for batched circuit tensors.
• Readout layers: Expectation, Sample, SampledExpectation, State, Unitary.
• Trainable circuit layers: PQC, ControlledPQC, NoisyPQC, NoisyControlledPQC.
• Backend choice, shot/repetition handling, control-input wiring, and noisy-layer behavior.

Read first

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md
• The bundled references capture the layer contracts, small recipes, and failure modes that the notebook examples and source tests establish.

Shared smoke helper

• Use ../../scripts/tfq_smoke_check.py for a quick import plus tiny one-qubit layer sanity check.
• Whole-notebook execution is not the default runtime path here; use the distilled notebook recipes in references/workflows.md instead.

Route away when

• The problem is about circuit tensor conversion, backend execution ops, or low-level op selection; use the tensor-ops-and-execution sub-skill.
• The problem is about differentiators, gradient plumbing, or optimizer loops; use the differentiation-and-optimizers sub-skill.
• The problem is about dataset loading or tutorial-scale execution; use the datasets-and-tutorials sub-skill.

Fast prompt mapping

• "Append a helper circuit" -> AddCircuit.
• "Read expectation / sample / state / unitary outputs" -> the executor layers.
• "Build a variational quantum layer" -> PQC.
• "Drive a circuit from classical features" -> ControlledPQC.
• "Add noise and train" -> NoisyPQC or NoisyControlledPQC.
• "Noisy or sampled readout on a fixed circuit" -> Expectation or SampledExpectation.
• "Need a custom backend object" -> check the layer's accepted backend class before wiring it in.