@ericyocam6Shipped: Quantum AI and Cryptanalysis on Real IBM Quantum Hardware
An open-source quantum computing research proof of concept investigating quantum cryptanalysis and quantum machine learning using IBM's quantum computing ecosystem.
The project combines educational demonstrations of Shor's algorithm for small-number factorization with Quantum Neural Network (QNN) training and evaluation.
Experiments include quantum circuit construction, measurement analysis, classical post-processing, and QNN classification performance on real quantum hardware.
The repository documents practical challenges associated with current noisy quantum systems, including unstable training, limited classification accuracy, and quantum hardware constraints.
The experiments demonstrate the difference between idealized quantum algorithms and practical implementations using available quantum computing resources.
Designed for AI researchers, quantum developers, cybersecurity professionals, and educators exploring quantum machine learning and the future implications of quantum computing for cryptography.
The factorization examples use small educational values and do not demonstrate the ability to break production RSA encryption.
Comments2
Writing down where training fell apart on real IBM machines is the part a security leader worried about quantum headlines will actually read. Will you rerun these tests as IBM releases bigger machines and publish how the results change each time?
Great question, Ryan! I agree that tracking how these experiments perform as IBM releases more capable quantum hardware would be valuable. Comparing training stability, classification accuracy, execution time, and noise across hardware generations could provide useful insights into the practical progress of quantum machine learning.
I haven't established a recurring testing schedule, but rerunning these experiments as hardware improves would be a worthwhile direction for future research. It would also help distinguish genuine improvements in quantum capabilities from the hype surrounding the technology. Appreciate the suggestion!
2 comments
Writing down where training fell apart on real IBM machines is the part a security leader worried about quantum headlines will actually read. Will you rerun these tests as IBM releases bigger machines and publish how the results change each time?
Great question, Ryan! I agree that tracking how these experiments perform as IBM releases more capable quantum hardware would be valuable. Comparing training stability, classification accuracy, execution time, and noise across hardware generations could provide useful insights into the practical progress of quantum machine learning.
I haven't established a recurring testing schedule, but rerunning these experiments as hardware improves would be a worthwhile direction for future research. It would also help distinguish genuine improvements in quantum capabilities from the hype surrounding the technology. Appreciate the suggestion!