9 citations · 10 across the 7 of their papers we have counts for
9 papers
Hybrid quantum-classical attention for histopathology-based molecular profiling in data-limited cancers
Kahn Rhrissorrakrai, Aritra Bose, Aldo Guzman-Saenz +2
Molecular profiling from routine histopathology could expand access to precision oncology when sequencing is unavailable, tissue is limited, or training cohorts are small. We devel…
A Quantum Reservoir for Neurodynamical Forecasting
Annemarie Wolff, Kathleen Hamilton, Kahn Rhrissorrakrai +3
Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical re…
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…
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…