Call for Papers

Call for contributions

We invite researchers to submit papers that address various aspects of Quantum Machine Learning (QML) applied to healthcare. The workshop calls for research contributions addressing, but not limited to, the following topics:

• Quantum embedding and hybrid algorithms for medical image analysis;

• QML applications across various stages of healthcare, including screening, diagnostics, and therapeutic interventions;

• Operational challenges in integrating QML solutions within clinical workflows;

• Ethical frameworks and guidelines to ensure trustworthiness and human-centered design of quantum healthcare technologies;

• Advanced techniques for quantum feature mapping and error mitigation strategies to enhance the reliability and accuracy of quantum-driven processes. 

Submission Guidelines

Submissions may include both original contributions and previously published works that offer valuable insights or significant advancements in this thematic area. 

Papers for the "From Bits to Qubits: Quantum Machine Learning for Medical Breakthroughs" workshop must be submitted via EasyChair through the dedicated track. 

Each paper can be up to eight pages, including references, and will undergo a single-blind review process. 

Papers should be formatted according to Springer’s LNCS format, following  Springer’s guidelines for authors and using their proceedings templates, either for LaTeX or for Word. Springer’s proceedings LaTeX templates are also available in Overleaf. Authors are encouraged to include their ORCIDs.

Further details can be found in the AIME 2025 submission guidelines.