doc: add Braket SDK cheat sheet

[AdwaithaV]

Summary

This PR updates and completes the Amazon Braket SDK cheat sheet for #1255.

I focused on making the cheat sheet accurate against the current SDK, compact in the existing two-column style, and easier to extend with the missing sections requested in the issue.

Changes

• Added new cheat sheet sections for:

  • Program Sets
  • Emulators
  • Experimental Features
  • Reservations

• Updated the Hybrid Jobs section to include the current hybrid job decorator workflow.

• Refreshed outdated snippets across existing sections, including:

  • Circuits
  • Free parameters
  • Tasks and results
  • Devices and device properties
  • Simulators
  • LLDC / pulse control
  • AHS
  • Noise simulation
  • Pricing / tracking
  • Error mitigation
  • Qiskit provider
  • API calls

• Updated blocks.yml so the new sections are included in the rendered cheat sheet.

• I based this on the existing draft cheat sheet structure from feat: publish a cheat sheet via github pages #995, but kept this PR focused on the English SDK cheat sheet and contribution notes. I did not include the incomplete French translation files from the draft because the issue requirements focus on updating SDK content and adding the requested missing feature sections.

Verification

I reviewed the updated snippets against the Amazon Braket Python SDK API reference and the current repository source where needed.

For snippets that can run locally without AWS credentials, I ran local verification checks, including imports and executable examples for:

• LocalSimulator
• ProgramSet
• CircuitBinding
• ParameterSets
• experimental capability imports
• emulator imports
• Devices.Amazon.SV1

For AWS-dependent snippets, such as AWS devices, reservations, and hybrid jobs, I avoided submitting cloud tasks. Instead, I verified the relevant imports and checked public method signatures locally to confirm that the documented parameters are supported by the installed SDK.

Notes

Some examples intentionally use placeholders such as device_arn, reservation_arn, and circuit because executing those snippets requires AWS credentials, a configured AWS region, and valid Braket resources.

AI disclosure

I used ChatGPT to review documentation and identify commands to verify imports/signatures locally. I manually reviewed the suggested changes, checked the relevant SDK references, ran the local verification steps, and edited the final snippets before submitting.

@peterkomar-aws , could you kindly review this PR when you get a chance? I would appreciate any suggestions on the section ordering, snippet style, or any SDK-specific wording that should be adjusted.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 100.00%. Comparing base (53aa5b8) to head (4b09944).
⚠️ Report is 8 commits behind head on main.

Additional details and impacted files

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  Coverage   100.00%   100.00%           
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  Files          169       170    +1     
  Lines        10963     11011   +48     
  Branches      1412      1413    +1     
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+ Hits         10963     11011   +48     

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[peterkomar-aws]

Please add a script that generates the AI-cheat sheet and build the webpage so we can test it locally.

[AdwaithaV]

Sure thanks for the review will add that too

[AdwaithaV]

@peterkomar-aws I have added the requested AI cheat sheet generation script and updated the README with local build/testing instructions.

I also added a Gemfile for the Jekyll site, documented the Ruby/Bundler setup, and verified both the AI cheat sheet generation and the local Jekyll build/rendering with Bundler.

Please review it when you get a chance.

[AdwaithaV]

@peterkomar-aws I have added all the requirements. Please review it when you get a chance.