5 citations · 6 across the 4 of their papers we have counts for
8 papers
Data complexity signature predicts quantum projected learning benefit for antibiotic resistance
Kahn Rhrissorrakrai, Filippo Utro, Alex Milinovich +4
This study presents the first large-scale empirical evaluation of quantum machine learning for predicting antibiotic resistance in clinical urine cultures. Antibiotic resistance is…
Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods
Filippo Utro, Meltem Tolunay, Kahn Rhrissorrakrai +6
Chimeric antigen receptor (CAR) T-cells are T-cells engineered to recognize and kill specific tumor cells. Through their extracellular domains, CAR T-cells bind tumor cell antigens…
Quantum Ensembling Methods for Healthcare and Life Science
Kahn Rhrissorrakrai, Kathleen E. Hamilton, Prerana Bangalore Parthsarathy +4
Learning on small data is a challenge frequently encountered in many real-world applications. In this work we study how effective quantum ensemble models are when trained on small…
On Quantum Random Walks in Biomolecular Networks
Viacheslav Dubovitskii, Aritra Bose, Filippo Utro +1
Biomolecular networks, such as protein-protein interactions, gene-gene associations, and cell-cell interactions, offer valuable insights into the complex organization of biological…
Quantum machine learning framework for longitudinal biomedical studies
Maria Demidik, Filippo Utro, Alexey Galda +3
Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders…
Quantum Doubly Stochastic Transformers
Jannis Born, Filip Skogh, Kahn Rhrissorrakrai +3
At the core of the Transformer, the softmax normalizes the attention matrix to be right stochastic. Previous research has shown that this often de-stabilizes training and that enfo…