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notebook ( Quantum_diffusion_models_adversarials)
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ART: Quantum diffusion models adversarial
Last Notebook (Thursday, october 16, 2025, last update!):
Notebook link:update
As a bonus, you will find attached the complete notebook with “existence of mass gaps in Yang-Mills quantum theory”. (To make everything advanced on the adversarial aspect, I use Hugging Face transformers in learning the quantum classifier.)
Last Notebook (Tuesday, october 07, 2025, really last update!):
Notebook link:update
Hi guys @beat-buesser !, I can confirm with certainty that you can already close the pull request; I will never provide more reasons or information, unless you want it :) :) Thanks again guys you are the best!! Bye :)
This attached code applies a (QuantumDiffusionModel-Monte Carlo) method in a very advanced and realistic :) way with various cutting-edge methods. If you want to have fun with huge qubits, use different IBM supercomputers :( if you have them, because the code framework is designed with IBM tools :)
If you have any difficulty understanding the code, the ART team can help you better understand it (otherwise, the ART team can notify me with a message, and I will do my best to help you understand your concerns in depth :)
Best Notebook (Tuesday, September 16, 2025, last update!):
Notebook link:update
Hi guys @beat-buesser , thanks.
I just updated the notebook (more realistic and advanced).
I think Codecov Report team can already check the notebook's optimization and robustness.
Thanks everyone!
Description
This notebook illustrates how to use ART (adversarial-robustness-toolbox) to generate adversarial attacks using a quantum diffusion model approach.
Notebook update (Thursday, September 4, 2025):
Notebook link:update
If you'd like to deepen your understanding of the impact of adversarial attack approaches on quantum computing, I invite you to take a look at the IBM-ART brochure I've had the pleasure of compiling. I wish you a pleasant reading and hope that the mathematical foundations that will soon follow will meet your expectations.
cc: @beat-buesser
cc: @jakelishman
cc:@mtreinish
cc: NVIDIA : Quantum Diffusion Models
notebook link (Quantum diffusion models adversarials)