# Quantum Machine Learning Applied to the Classification of Diabetes

arXiv: https://arxiv.org/abs/2301.00109 · HTML: https://mlsanity.com/paper/2301.00109/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 31 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [A Gentle Introduction to Deep Learning in Medical Image Processing](https://mlsanity.com/paper/1810.05401/) (2018)
- [Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning](https://mlsanity.com/paper/2206.15284/) (2022)
- [Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems](https://mlsanity.com/paper/1911.04574/) (2019)
- [Study of Feature Importance for Quantum Machine Learning Models](https://mlsanity.com/paper/2202.11204/) (2022)
- [Machine Learning based Discrimination for Excited State Promoted Readout](https://mlsanity.com/paper/2210.08574/) (2022)
- [Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN](https://mlsanity.com/paper/2212.13161/) (2022)
- [Linear Discriminant Analysis with the Randomized Kaczmarz Method](https://mlsanity.com/paper/2211.05749/) (2022)
- [Simulation of Networked Quantum Computing on Encrypted Data](https://mlsanity.com/paper/2212.12953/) (2022)

## Built on by (most cited first, 2 of 2)

- [On Optimizing Hyperparameters for Quantum Neural Networks](https://mlsanity.com/paper/2403.18579/) (2024)
- [Quantum-Inspired Stacked Integrated Concept Graph Model (QISICGM) for Diabetes Risk Prediction](https://mlsanity.com/paper/2509.12259/) (2025)

## Abstract (arXiv)

Quantum Machine Learning (QML) shows how it maintains certain significant advantages over machine learning methods. It now shows that hybrid quantum methods have great scope for deployment and optimisation, and hold promise for future industries. As a weakness, quantum computing does not have enough qubits to justify its potential. This topic of study gives us encouraging results in the improvement of quantum coding, being the data preprocessing an important point in this research we employ two dimensionality reduction techniques LDA and PCA applying them in a hybrid way Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) in the classification of Diabetes.