QiML 2.0: Speed Ups, Scalability, and Performance for New Machine Learning Era

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QiML 2.0: Speed-Ups, Scalability, and Performance for New Machine Learning Era will feature quantum approximation methods of 'dequantized algorithms' developed by Ewin Tang and others that improve Speed-ups over prior Quantum-inspired machine learning studies using Recommendation systems, Supervised clustering, Principal component analysis, and more.

In addition, New utilities in the form of accessible quantum systems and complex systems by Tensor network approximations will be discussed in the seminar. Scalability and performance achievements, as well as insight regarding solutions to larger systems will also be presented.

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Sincerely,
CEO Kevin Kawchak

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References:
  1. ChemicalQDevice
  2. speedup
  3. scalability
  4. performance
  5. patientexperience
  6. customerexperience
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