Enhance notebook testing and API key retrieval#56
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- Updated `test_notebooks.py` to improve integration tests for Jupyter notebooks. - Added a timeout for notebook execution and improved error handling. - Skipped tests if the IONQ_API_KEY environment variable is not set. - Refactored notebook execution logic into a separate function for clarity. - Introduced `helpers.py` to centralize IonQ API key retrieval. - Implemented a function to get the API key from the environment or prompt the user interactively if not set.
- Add detailed logging to GitHub Actions workflow with step-by-step output - Add environment verification step to show Python version and packages - Add test summary step that runs even if tests fail - Enhance test_notebooks.py with detailed execution logging: - Show timestamp and duration for each notebook - Display progress with visual separators - Provide summary with pass/fail counts - Add better error reporting - Add pytest and nbconvert to environment.yml (required for tests) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add discovery job to find all notebooks dynamically - Use matrix strategy to run each notebook in parallel - Set fail-fast: false to see all failures - Add per-notebook execution logging with timestamps - Upload HTML artifacts for each notebook - Add summary job to report overall status - Significantly reduces total CI/CD time Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Replace qasm3_drawer with pyqasm.draw per qBraid SDK changelog - Add pyqasm to pip install dependencies - Update import statement and function call Fixes: ImportError: cannot import name 'qasm3_drawer' from 'qbraid.visualization' Reference: qBraid/qBraid#943 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add pyproject.toml with all dependencies for modern Python packaging - Create new GitHub Actions workflow using uv (test_notebooks_uv.yml) - Update README with uv installation instructions (recommended method) - Keep conda workflow as fallback option Benefits of uv: - 10-100x faster dependency resolution than pip/conda - Significantly faster package installation - Better caching and reproducible builds - Compatible with entire PyPI ecosystem Expected improvement: Reduce dependency installation from ~60s to ~10s Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Performance comparison (average per notebook): - Conda: ~3 minutes - uv: ~1 minute - Improvement: 2.8x faster (66% reduction) Specific improvements: - api.ipynb: 3m5s → 47s (3.9x faster) - qbraid.ipynb: 3m11s → 1m4s (3x faster) - cuda-quantum: 3m9s → 1m24s (2.25x faster) - pennylane.ipynb: 3m7s → 54s (3.5x faster) - cirq.ipynb: 2m58s → 57s (3.1x faster) - qiskit.ipynb: 2m53s → 55s (3.2x faster) Benefits: ✓ Much faster dependency resolution and installation ✓ More efficient CI/CD minute usage ✓ Modern Python packaging with pyproject.toml ✓ Better developer experience The conda environment.yml is kept for local development compatibility. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Updated
test_notebooks.pyto improve integration tests for Jupyter notebooks.Introduced
helpers.pyto centralize IonQ API key retrieval.