---

name: datasets-and-tutorials description: "Route TFQ dataset helpers and notebook-style workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Datasets and Tutorials

Use this skill for TensorFlow Quantum prompts about dataset loaders, tuple outputs, and the small notebook-derived recipes used in the public tutorials.

Route here when the user asks about

• tfq.datasets.excited_cluster_states
• tfq.datasets.tfi_chain
• tfq.datasets.xxz_chain
• tuple contents such as circuits, labels, pauli_sums, and SpinSystemInfo
• notebook snippets from hello_many_worlds, gradients, noise, qcnn, quantum_data, quantum_reinforcement_learning, and research_tools

Do not use this skill for

• low-level tensor/backend mechanics; hand off to tensor-ops-and-execution
• broader Keras layer design beyond the notebook recipes here; hand off to keras-quantum-layers
• full notebook validation or maintainer scrubbing as a default runtime step
• Bazel/source-build or benchmark workflows

Read first

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md

Fast answer rule

If the prompt is short and factual, answer from the API reference. If it asks for a tiny worked example, use the workflow snippets. If it reports a failure, use troubleshooting before changing the recipe.

Shared smoke helper

For a tiny packaged import and round-trip check, run python scripts/tfq_smoke_check.py --quick --datasets from the root tensorflow-quantum skill directory instead of running whole notebooks.

Evidence anchors

• Public package docs and tutorials distilled into this sub-skill's bundled references.
• The installed-package inspection used during construction to confirm the public TFQ dataset signatures and the tiny cluster-state smoke behavior.
• The source repository tests that establish dataset shapes, errors, and download behavior, summarized here rather than referenced directly at runtime.