Add CPU accelerator option and update dependencies

[JeremieGince]

Description

This pull request updates three tutorial notebooks to explicitly set the accelerator parameter to "cpu" for both the Lightning and AutoML pipelines, ensuring consistent hardware usage regardless of GPU availability. Additionally, the nif_deep_learning.ipynb notebook receives refactoring to use new quantum operation classes, includes execution metadata and outputs for improved reproducibility, and displays detailed training and evaluation results inline.

Hardware configuration and pipeline consistency:

• Added accelerator="cpu" to the pipeline initialization in notebooks/automl_pipeline_tutorial.ipynb, notebooks/ligthning_pipeline_tutorial.ipynb, and notebooks/nif_deep_learning.ipynb to force CPU usage and avoid GPU selection issues. [1] [2] [3] [4]

Quantum circuit refactoring:

• Replaced quantum operation imports and usage in the NIFDL model from SptmfRxRx/SptmFHH to CompRxRx/CompHH for improved clarity or functionality. [1] [2]

Notebook execution and reproducibility:

• Added execution metadata (timestamps and execution counts) to most code cells in notebooks/nif_deep_learning.ipynb for better reproducibility and tracking of cell runs. [1] [2] [3] [4] [5] [6]

Inline output and results reporting:

• Updated notebooks/nif_deep_learning.ipynb to capture and display detailed training, validation, and test metrics, including model summaries, progress bars, and metric tables, directly within the notebook outputs. [1] [2]

These changes make the tutorials more robust across different hardware environments, improve the quantum model implementation, and enhance the clarity and reproducibility of notebook results.

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Checklist

Please complete the following checklist when submitting a PR. The PR will not be reviewed until all items are checked.

• All new features include a unit test.
  Make sure that the tests passed and the coverage is
  sufficient by running pytest tests --cov=src --cov-report=term-missing.
• All new functions and code are clearly documented.
• The code is formatted using Black.
  You can do this by running black src tests.
• The imports are sorted using isort.
  You can do this by running isort src tests.
• The code is type-checked using Mypy.
  You can do this by running mypy src tests.

[github-actions]

☂️ Python Coverage

current status: ✅

Overall Coverage

Lines	Covered	Coverage	Threshold	Status
882	859	97%	90%	🟢

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updated for commit: d6cd179 by action🐍