---

name: tensorflow-quantum description: "Guide TensorFlow Quantum workflows for circuit tensors, Keras quantum layers, differentiators, datasets, and tutorial recipes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

TensorFlow Quantum

Use this skill when a prompt is about the TensorFlow Quantum package or the public tensorflow_quantum module.

Route here when the user asks about

• package install, import, version, or TF_USE_LEGACY_KERAS setup
• circuit tensor conversion, raw execution getters, noisy execution, or tfq.math
• tfq.layers model wiring, readout layers, or quantum-classical Keras models
• differentiators, gradient plumbing, or tiny optimizer loops
• dataset helpers, notebook-derived recipes, or tutorial-style examples

Sub-skill map

• sub-skills/tensor-ops-and-execution/SKILL.md for tfq.convert_to_tensor, tfq.from_tensor, raw execution ops, tfq.noise, tfq.math, and quantum-concurrent op mode questions
• sub-skills/keras-quantum-layers/SKILL.md for tfq.layers, append/prepend wiring, PQC/ControlledPQC families, and noisy readout layers
• sub-skills/differentiation-and-optimizers/SKILL.md for tfq.differentiators, tfq.optimizers, and gradient/parameter-search workflows
• sub-skills/datasets-and-tutorials/SKILL.md for tfq.datasets helpers and the notebook-style recipe summaries

Start here

• references/installation-and-compatibility.md
• references/api-overview.md
• references/troubleshooting.md
• references/repo-provenance.md when checking whether this skill still matches the checkout
• scripts/tfq_smoke_check.py

Fast path

If the prompt is short and factual, answer from references/api-overview.md. If it asks for setup or compatibility, read references/installation-and-compatibility.md first. If it reports an error, use references/troubleshooting.md before changing the recipe.

Minimal setup reminder

• TensorFlow Quantum supports Python 3.10-3.12.
• Set TF_USE_LEGACY_KERAS=1 before importing TensorFlow or TFQ.
• Install the package first, then run python scripts/tfq_smoke_check.py --quick.
• Add --layers, --datasets, --differentiators, or --math when you need a slightly deeper smoke.

Shared route rule

Do not force a low-level tensor or backend issue into a layers or dataset route. Use the owning sub-skill so the future agent gets the right reference and smoke path on the first try.