Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in networks and systems

A Comparative Analysis of Classical and Quantum Machine Learning Approaches in Healthcare Data Analytics

Ashly C. Dhanu, P. C. Chaitra, M. Manohar

Published Oct 3, 2025• 0 citations
Publisher
DOI
10.1007/978-3-032-05802-7_10

Related papers

Quantum Machine Learning Enabled Privacy Preserving Healthcare Data Analytics

Mathivanan P, D. Mohanapriya

•Jan 1, 2026•0 citations

Quantum Machine Learning in Healthcare

B. Goutham, P. Kannan, C. Visali

The growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.

Advances in computational intelligence and robotics book series•Oct 23, 2026•0 citations

Quantum machine learning models in healthcare: future trends and challenges in healthcare

Arnav Shailesh Sonavane, Shweta Jaiswar, Maitri Mistry +2 more

Elsevier eBooks•Jan 1, 2025•13 citations

AI driven quantum machine learning for predictive healthcare analytics

Lakshita Aggarwal, Puneet Goswami

Biomedical Signal Processing and Control•Feb 19, 2026•2 citations

Diagnosis, Prediction, and Risk Factor Analysis of Multiple Sclerosis Disease using Quantum Machine Learning Classifier

Jyoti Garg, Vaishali Mehta Wadhwa, Harminder Kaur +3 more

Multiple sclerosis (MS) is a condition that affects the brain or central nervous system, which can result in issues with the optic nerve, brain, and spinal cord. Multiple sclerosis is thought to affect 2.8 million people. This research focuses on the diagnosis of multiple sclerosis using Quantum machine learning (QML) classifiers. Several models have been created utilizing various data sets, such as clinical and magnetic resonance imaging (MRI) data. The quantum machine learning has emerged by the merging of classical machine learning and quantum information processing as a result of the superiority and advancement of quantum computing in various fields (such as encryption, machine learning, and healthcare). The experimental results present a comparative analysis of classical machine learning and quantum machine learning algorithms for the problem under consideration.

International Conference on Pervasive Computational Technologies (ICPCT)•Feb 8, 2025•4 citations

A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface temperature

Trilok Nath Pandey, Vishvajeet Ravalekar, Sidharth D. Nair +1 more

This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study's findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.

Scientific Reports•Aug 4, 2025•11 citations
The growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.
Multiple sclerosis (MS) is a condition that affects the brain or central nervous system, which can result in issues with the optic nerve, brain, and spinal cord. Multiple sclerosis is thought to affect 2.8 million people. This research focuses on the diagnosis of multiple sclerosis using Quantum machine learning (QML) classifiers. Several models have been created utilizing various data sets, such as clinical and magnetic resonance imaging (MRI) data. The quantum machine learning has emerged by the merging of classical machine learning and quantum information processing as a result of the superiority and advancement of quantum computing in various fields (such as encryption, machine learning, and healthcare). The experimental results present a comparative analysis of classical machine learning and quantum machine learning algorithms for the problem under consideration.
This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity of classical machine learning algorithms on traditional hardware to their quantum counterparts on quantum computers. As the amount and complexity of time-series data in numerous fields continues to expand, the investigation of advanced computational models becomes critical for efficient analysis and prediction. We employ a time-series dataset that include temperature records from different nations throughout the world spanning the previous half of the century. The study compares the performance of classical machine learning algorithms to quantum algorithms, which use the concepts of superposition and entanglement to handle subtle temporal patterns in time-series data. This study attempts to reveal the different benefits and drawbacks of quantum machine learning in the time-series domain through rigorous empirical analysis. The findings of this study not only help to comprehend the applicability of quantum algorithms in real-world contexts, but they also open the way for future advances in utilizing quantum computing for increased time-series analysis and prediction. This study's findings could have ramifications in industries ranging from finance to healthcare, where precise forecasting using time-series data is critical for informed decision-making.