Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets
This study suggests that data re-uploading algorithms can perform comparably to classical approaches in specific contexts, particularly with low-dimensional data. While optimization can enhance performance, further improvements in quantum hardware and algorithmic development are likely needed before QML can be effectively applied in laboratory medicine and broader biomedical research.
Authors
- Thomas J S Durant (ORCID: https://orcid.org/0000-0003-1107-8148)
- Sarah Dudgeon (ORCID: https://orcid.org/0000-0001-9073-1293)
- Brent Nelson (ORCID: https://orcid.org/0000-0001-6874-5306)
- Lucila Ohno-Machado
- Seung Joo (Erin) Lee
- Elizabeth Knight
- H. Patrick Young
- Wade L Schulz
Institutions
- University of Minnesota (US)
- Yale University (US)
- Hennepin Healthcare Research Institute (US)
Publication Details
- Journal
- Clinical Chemistry
- Published
- 2025-12-18
- DOI
- https://doi.org/10.1093/clinchem/hvaf192
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00