Title: Quantum simulation scenarios and disease classification behaviour on diabetes data

Authors: Ajeet Singh; N.D. Patel

Addresses: School of Computing Science and Engineering (SCSE), Vellore Institute of Technology – VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh – 466114, India ' School of Computing Science and Engineering (SCSE), Vellore Institute of Technology – VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh – 466114, India

Abstract: In quantum mechanics, the state of a particle can be fully characterised for all future periods based on the beginning conditions and knowledge of the potential occupied by the particle. This paper presents an overview of the integration of statistical machine learning and quantum mechanics. Furthermore, we provide simulation scenarios, classification behaviour, and empirical observations on healthcare data through the utilisation of Feynman diagrams (Feynman et al., 2010) and QLattice (Abzu, 2022). The experimental simulation is carried out in the following instances: 1) changing the number of updating loops; 2) calling the qgraph.fit function multiple times before updating the QLattice; 3) fitting and selecting graphs according to different loss functions; 4) setting the graphs max depth to comparatively higher or smaller values. The paper concludes by summarising the observations made throughout the study and discussing the potential for future work in this field.

Keywords: path integral formulation; quantum machine learning; QGraph; registers; interactions; binary classification; simulation; healthcare.

DOI: 10.1504/IJAHUC.2023.134604

International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.44 No.2, pp.109 - 123

Received: 12 Feb 2023
Accepted: 10 May 2023
Published online: 30 Oct 2023 *

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