US Patent Application 17825614. METHOD AND SYSTEM OF GENERATING A CLASSICAL MODEL TO SIMULATE A QUANTUM COMPUTATIONAL MODEL VIA INPUT PERTURBATION TO ENHANCE EXPLAINABILITY simplified abstract

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METHOD AND SYSTEM OF GENERATING A CLASSICAL MODEL TO SIMULATE A QUANTUM COMPUTATIONAL MODEL VIA INPUT PERTURBATION TO ENHANCE EXPLAINABILITY

Organization Name

International Business Machines Corporation

Inventor(s)

Vladimir Rastunkov of Mundelein IL (US)

[[:Category:Frederik Frank Fl�ther of Schlieren (CH)|Frederik Frank Fl�ther of Schlieren (CH)]][[Category:Frederik Frank Fl�ther of Schlieren (CH)]]

Amol Deshmukh of New York NY (US)

Shikhar Kwatra of San Jose CA (US)

METHOD AND SYSTEM OF GENERATING A CLASSICAL MODEL TO SIMULATE A QUANTUM COMPUTATIONAL MODEL VIA INPUT PERTURBATION TO ENHANCE EXPLAINABILITY - A simplified explanation of the abstract

This abstract first appeared for US patent application 17825614 titled 'METHOD AND SYSTEM OF GENERATING A CLASSICAL MODEL TO SIMULATE A QUANTUM COMPUTATIONAL MODEL VIA INPUT PERTURBATION TO ENHANCE EXPLAINABILITY

Simplified Explanation

The patent application describes a method for generating a classical model to simulate a quantum computational model.

  • Input dataset into a quantum computational model implemented on a quantum computer.
  • Compute output results using the quantum computer.
  • Introduce a variation to at least a portion of the dataset into the quantum computer.
  • Compute updated output results based on the variation using the quantum computer.
  • Generate a classical twin model of the quantum computational model based on the relationship between the output results, updated output results, and the dataset.


Original Abstract Submitted

A method of generating a classical model to simulate a quantum computational model includes 1) inputting into a quantum computational model a dataset, the quantum computational model being implemented on a quantum computer, 2) computing output results with the quantum computational model using the quantum computer, 3) introducing a variation to at least a portion of the dataset into the quantum computer, 4) computing updated output results of the quantum computational model based on the variation of the at least the portion of the dataset using the quantum computer, and 5) generating a classical twin model of the quantum computational model based on a relationship of the output results and updated output results to the dataset from the quantum computational model.

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