{"id":4748,"date":"2026-04-09T20:12:58","date_gmt":"2026-04-10T00:12:58","guid":{"rendered":"https:\/\/sites.miamioh.edu\/cse-senior-design-expo\/?p=4748"},"modified":"2026-04-14T19:48:11","modified_gmt":"2026-04-14T23:48:11","slug":"quantum-computing-research-project","status":"publish","type":"post","link":"https:\/\/sites.miamioh.edu\/cse-senior-design-expo\/2026\/04\/quantum-computing-research-project\/","title":{"rendered":"QELM Application to Healthcare"},"content":{"rendered":"\n
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Research Objective:<\/td>\n Evaluate whether a simple Quantum Extreme Learning Machine model can classify lung nodule malignancy from radiomic features more effectively than classical baselines.<\/td>\n <\/tr>\n\n \n\n
Researcher:<\/td>\n Jose Roberto Fabre Segundo<\/td>\n <\/tr>\n \n
Advisor:<\/td>\n Dr. Qiang Guan, Kent State<\/td>\n <\/tr>\n\n
Abstract<\/td>\n Lung cancer is the leading cause of cancer-related death worldwide, and automated malignancy classification of pulmonary nodules from CT scans is a high-value clinical problem. This work investigates whether a Quantum Extreme Learning Machine can extract more discriminative representations of radiomic data than classical methods while using a fraction of the input features. A 10-qubit circuit with redundant angle encoding and a random transverse-field Ising reservoir was evaluated on 526 nodules from the LIDC-IDRI dataset, with 56 features reduced to 4 via ANOVA selection. The QELM achieved the highest AUC (0.971) and accuracy (91.5%) across all models, outperforming five classical baselines, including Logistic Regression, MLP, and Random Forest, that were trained on all 56 features. The results demonstrate that the 512-dimensional Hilbert space of the Ising reservoir generates nonlinear feature representations sufficient to surpass classical performance with a 14x reduction in input dimensionality, establishing feature efficiency as the primary empirical finding of this study.<\/td>\n <\/tr>\n <\/table>\n\n