Add generative quantum advantage tutorial for issue #909.

[rosspeili]

New TFQ tutorial based on arxiv:2509.09033 on generative quantum advantage, walks through a small 8-qubit demo, build a shallow ansatz, create training bitstrings, train with TFQ (get_state_op + parameter shift), and sample with get_sampling_op.

Also updates docs/_book.yaml and adds CI runtime caps in scripts/test_tutorials.py.

Closes #909

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[gemini-code-assist]

Code Review

This pull request introduces a new tutorial notebook, generative_quantum_advantage.ipynb, demonstrating generative quantum advantage using TensorFlow Quantum, along with registering it in _book.yaml and updating test_tutorials.py to scale down parameters for faster testing. A critical mathematical bug was identified in the parameter shift gradient calculation: the parameter shift rule is incorrectly applied directly to the non-linear negative log-likelihood loss function rather than the underlying probabilities, which leads to incorrect gradients. A code suggestion is provided to resolve this issue.

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[rosspeili]

@mhucka let me know how this looks, or if I should add more sections, or anything else I might be forgetting. Thanks in advance <3