loading page

On a quantum inspired approach to train machine learning models
  • Jean Michel Sellier
Jean Michel Sellier

Corresponding Author:[email protected]

Author Profile


In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems. This represents a drastic departure from the standard approach which, to these days, is based on the use of actual physical quantum systems. Thus, to provide a clear context, a proper introduction to the field of quantum machine learning is first provided. Then, we proceed with a detailed description of our proposed method. To conclude, some preliminary, yet compelling, results are presented and discussed. Although at a seminal stage, the author firmly believes that this approach could represent a valid and robust alternative to the way machine learning models are trained today.
29 Aug 2023Submitted to Applied AI Letters
30 Aug 2023Submission Checks Completed
30 Aug 2023Assigned to Editor
01 Sep 2023Reviewer(s) Assigned
04 Oct 2023Review(s) Completed, Editorial Evaluation Pending
06 Oct 2023Editorial Decision: Revise Major
20 Oct 20231st Revision Received
24 Oct 2023Submission Checks Completed
24 Oct 2023Assigned to Editor
30 Oct 2023Reviewer(s) Assigned
14 Nov 2023Review(s) Completed, Editorial Evaluation Pending