Quantum Computing in a Diagnostic-First Quantum Residual Boosting Framework for Clinical Survival Analysis in...

Objective: Survival prediction in oncology and cardiology requires models that can capture nonlinear prognostic structure while remaining interpretable, calibrated, and clinically safe. This study develops and evaluates a diagnostic-first hybrid quantum-classical framework for right-censored survival analysis. Methods: We introduce KTA-Survival (Kernel-Target Alignment for survival), a pre-training feasibility diagnostic that adapts kernel-target alignment to censored outcomes by comparing a quantum fidelity kernel with a concordance-based survival target kernel. We then propose QResid-Boost (Quantum Residual Boosting), a Cox-LASSO-anchored residual framework in which a variational quantum circuit is trained on martingale residuals through a Quantum-Skip-Residual architecture. A sigmoid-bounded scalar gate, α, constrains the quantum contribution and allows the model to reduce to the classical baseline when the residual signal is uninformative. The framework was evaluated on GBSG2 (German Breast Cancer Study Group 2; n = 686), FLChain (serum free light chain; n = 1500), WHAS500 (Worcester Heart Attack Study; n = 500), and a synthetic Weibull positive-control dataset containing high-frequency periodic interactions. Results: On the GBSG2 hold-out partition, Random Survival Forest achieved the highest concordance (C = 0.7188), followed by the Stacking ensemble (C = 0.7128), Cox-LASSO (C = 0.7019), and QResid-Boost (C = 0.7016). The leading classical and hybrid models did not differ significantly by paired bootstrap testing, whereas all outperformed the pure quantum variants. In the synthetic positive-control cohort, QResid-Boost improved over Cox-LASSO by ΔC = +0.0397, demonstrating that the quantum residual can add value when nonlinear periodic structure remains after the linear baseline. KTA-Survival yielded positive ΔKTA values across the evaluated datasets and correctly identified the regime in which the quantum residual produced its largest measurable gain. Conclusions: The proposed diagnostic-first framework reframes quantum survival modelling as a gated enrichment strategy rather than an unconstrained replacement for classical risk models. In low-dimensional clinical cohorts where linear structure already explains most prognostic signal, the framework behaves conservatively; when residual nonlinear structure is present, it can provide measurable improvement without uncontrolled model drift.