Installs into .claude/skills of the current project.
Are you the author of Keras Quantum Layers?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/vectorspacelab-keras-quantum-layers)
---
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.
