C. Albornoz, G. Alonso, M. Andrenkov, P. Angara, A. Asadi, A. Ballon, S. Bapat, L. Botelho, I. De Vlugt, O. Di Matteo, P. Downing, P. Finlay, A. Fumagalli, A. Gardhouse, J. Geoffrion, N. Girard, A. Hayes, J. Izaac, R. Janik, T. Kalajdzievski, A. Kanwar Singh, A. Khomchenko, N. Killoran, I. Kurečić, O. Landon-Cardinal, A. Martin, D. Nino, A. Otto, C. Pere, J. Pickering, K. Renaud, J. Soni, D. Wakeham, L. Young. PennyLane Codebook. 2024. https://pennylane.ai/codebook

Resources¶

• Check out {Link: Pennylane Cookbook https://pennylane.ai/codebook/learning-paths}!
  
• Check out {Link: Discover new ideas faster. https://pennylane.ai/}![1][2][3]
• Check out {Link: Papers With Code https://portal.paperswithcode.com/}!
  
• Check out {Link: Math for Machine Learning https://github.com/Ryota-Kawamura/Mathematics-for-Machine-Learning-and-Data-Science-Specialization/tree/main/}!
  
• Check out {Link: Hugging Face https://huggingface.co/}!
  
• Check out {Link: How to contribute Open Source project https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project }!
  
• Check out {Link: NotebookLM https://notebooklm.google.com/}!
  
• Check out {Link: IBM Quantum Learning https://learning.quantum.ibm.com/}!
  
• Check out {Link: dask-python libray https://www.dask.org/}!
  

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