Quantum Optimization for Feature Selection in Biomedical Data
Presenters: Henry Liu, Rensselaer Polytechnic Institute, and Robert Lahmann, Kipu Quantum
When: Wednesday, October 21, 2026 10:00 – 11:30am
Format: Online workshop
Audience: Open to all
Join via Microsoft Teams
Please join us for an online workshop on applying quantum optimization to feature selection in biomedical data.
Henry Liu of Rensselaer Polytechnic Institute will present research on feature selection for autism spectrum disorder (ASD) metabolomics data. The work uses a QUBO formulation and quantum optimization to identify a small set of informative biomarkers from a rapidly growing space of possible feature combinations. Henry will discuss the biomedical motivation, explain why feature selection becomes increasingly difficult as the search space expands, and describe how the problem is formulated for quantum optimization.
The presentation will use Kipu Quantum’s Iskay and Miray platforms. Henry will compare results from Iskay—including solution quality, runtime, and convergence toward optimal solutions—with classical feature-selection methods and QAOA implementations using IBM’s Qiskit. If time permits, he will demonstrate the workflow in Kipu’s Miray using a small dataset from the associated paper.
Robert Lahmann of Kipu Quantum will then provide a technical perspective on Bias-Field Digitized Counterdiabatic Quantum Optimization (BF-DCQO), the algorithm used in the study. He will explain how BF-DCQO is designed to run effectively on today’s quantum hardware and how it supports the feature-selection results presented in the workshop.
The associated paper is available at:
arxiv.org/abs/2609.30256
This event is open to all interested students, faculty, researchers, and practitioners. No prior experience with quantum computing or biomedical data analysis is required.