Quantum Machine Learning for Rare Disease Identification
We cordially invite you to submit your research papers for the upcoming “QML 2025 – Quantum Machine Learning for Rare Disease Identification” for the 17th IEEE International Conference on Knowledge and Systems Engineering (KSE 2025). This session aims to bring together cutting-edge research at the intersection of quantum computing, machine learning, and rare disease diagnostics, emphasizing novel solutions to the challenges of scarce data, diagnostic complexity, and predictive modelling. Rare diseases, due to their low prevalence and heterogeneous clinical presentations, remain difficult to diagnose accurately and promptly. With approximately 80% of rare diseases having genetic origins and millions of individuals affected worldwide, early and precise identification remains a critical healthcare need. Quantum Machine Learning (QML) offers unprecedented potential to model complex biological systems and small, incomplete datasets more efficiently than classical methods, opening new pathways for breakthroughs in rare disease research.
Scope and Topics
This special session will cover theoretical foundations, algorithmic innovations, and applied research within Quantum Machine Learning and its integration into rare disease identification systems. Topics include, but are not limited to:
Foundations of QML for Healthcare
- Quantum algorithms for medical diagnostics
- Quantum-enhanced machine learning techniques
- Quantum data processing methods for large-scale biological and clinical datasets
Applications in Rare Disease Identification
- Case studies on QML applications in rare disease detection
- Hybrid quantum-classical models for biomedical research
- Comparative analyses between classical and quantum approaches in healthcare diagnostics
Challenges, Ethics, and Future Directions
- Ethical implications and challenges in applying QML in healthcare
- Integration of quantum computing into existing healthcare infrastructures
- Future directions of QML in precision medicine and personalized healthcare
We particularly encourage interdisciplinary contributions from researchers in quantum computing, machine learning, bioinformatics, genomics, and clinical medicine.
Importance and Impact
The identification and diagnosis of rare diseases represent a major bottleneck in medical practice, largely due to the scarcity of patient data and the complexity of clinical manifestations. Quantum Machine Learning provides a transformative opportunity to redefine how healthcare professionals approach rare disease diagnostics by enabling faster, more accurate pattern recognition and predictive modelling, even from limited datasets.
This special session aims not only to highlight the latest research and methodologies but also to foster collaborations across disciplines, catalysing future innovations. Accepted papers from this session could lay the groundwork for real-world quantum-enhanced diagnostic systems within the next decade, fundamentally advancing personalized medicine and healthcare AI.
Keywords
- Quantum Machine Learning
- Rare Disease Diagnosis
- Quantum Algorithms for Healthcare
- Quantum Data Processing
- Personalized Medicine
- Hybrid Quantum-Classical Models
- Biomedical Data Analysis
- Quantum Computing in Healthcare
- Machine Learning in Precision Medicine
- Ethical Challenges in Quantum Healthcare
Session Organizers:
- Fahad Ahmad, University of Portsmouth, UK, fahad.ahmad@port.ac.uk
Paper Submission
The papers of this session will be printed in proceeding of main conference which will be published by IEEE and be available at the conference. Papers must be submitted through the online submission system:
- Submission: http://www.easychair.org/conferences/?conf=kse2025
- Select Session: Quantum Machine Learning for Rare Disease Identification
Authors are invited to submit papers of up to 6 pages, written in English, in PDF format and compliant with the IEEE standard. The submissions will be peer-reviewed for originality and scientific quality.
(https://www.ieee.org/conferences/publishing/templates.html).