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 computer science

Exploring Quantum Machine Learning for Early Disease Detection: Perspectives, Challenges, and Opportunities

Madhusudan Singh, Irish Singh, Dhananjay Singh

Published Jan 1, 2024• 5 citations
Publisher
DOI
10.1007/978-3-031-53827-8_22

Related papers

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

Quantum Machine Learning in Crop Disease Monitoring: Opportunities and Challenges to Practical Implementation

Yue Wu, Attila Balázs Nagy, Zoltán Rajnai +2 more

Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.

IEEE International Conference on Computational Cybernetics (ICCC)•Apr 9, 2025•6 citations

Glance at Quantum Innovations in Crop Pest and Disease Detection: Bridging Physics and Agriculture

Attila Balázs Nagy, Yue Wu, Katalin Takács-György +2 more

Agriculture faces increasing risks in ensuring global food security, with challenges such as climate change, biodiversity loss, and plant disease and pest outbreaks posing significant threats to crop yields. These factors are compounded by growing global populations, resource limitations, and market fluctuations. Traditional methods for crop pest and disease monitoring, often reliant on manual surveys and basic machine learning models, are inadequate in addressing these challenges due to limited scalability and accuracy. This research explores the potential application of quantum machine learning (QML) for crop pest and disease monitoring, leveraging the advanced computational capabilities of quantum computers to improve detection accuracy, efficiency, and scalability. The study reviews existing monitoring systems, recent advancements in quantum computing technologies, and the integration of QML algorithms, which offer promising solutions for handling large, complex agricultural datasets. While the practical application of QML in agriculture is still in its early stages, its theoretical advantages indicate significant potential for future smart agricultural systems.

IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI)•Jan 23, 2025•7 citations

Quantum Machine Learning Algorithms for Diagnostic Applications: A Review

Shruti S. Pophale, Amit Gadekar

Lecture notes in electrical engineering•Jan 1, 2023•3 citations

Quantum machine learning: Challenges and Opportunities

Leonard Wossnig, Simone Severini B

Bulletin of the American Physical Society•Mar 6, 2019•1 citation

Quantum Machine Learning for Early Detection of Mental Health Disorders: Foundations, Architectures, Security and Future Clinical Applications

Fabrice Kameni, Paramita Basak Upama, Mia Md. Raihan +1 more

Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental healthcare.

IntechOpen eBooks•Oct 15, 2026•0 citations
Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.
Agriculture faces increasing risks in ensuring global food security, with challenges such as climate change, biodiversity loss, and plant disease and pest outbreaks posing significant threats to crop yields. These factors are compounded by growing global populations, resource limitations, and market fluctuations. Traditional methods for crop pest and disease monitoring, often reliant on manual surveys and basic machine learning models, are inadequate in addressing these challenges due to limited scalability and accuracy. This research explores the potential application of quantum machine learning (QML) for crop pest and disease monitoring, leveraging the advanced computational capabilities of quantum computers to improve detection accuracy, efficiency, and scalability. The study reviews existing monitoring systems, recent advancements in quantum computing technologies, and the integration of QML algorithms, which offer promising solutions for handling large, complex agricultural datasets. While the practical application of QML in agriculture is still in its early stages, its theoretical advantages indicate significant potential for future smart agricultural systems.
Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental healthcare.