QBioBench: Benchmark of quantum machine learning for peripheral biosignals on real hardware

This is the authors' abstract. We don't add key points for this paper.

Claims of quantum advantage in machine learning often rest on classical comparators that lack the same features, on simulation alone, and on unreported hardware cost. QBioBench is a benchmark of quantum machine learning on peripheral biosignals that holds quantum and classical models to the same features and reports calibration and hardware cost. It comprises three clinical tasks, 12-lead ECG diagnosis (PTB-XL), single-lead apnea detection (Apnea-ECG), and wearable stress detection (WESAD), each reduced to six to eight shared dimensions. A factorial grid of projected and fidelity quantum kernels and variational classifiers is screened in noisy simulation, and the promoted configurations are evaluated in an exploratory study on an IBM Nighthawk r2 processor on the full test pools of 510 apnea minutes and 512 PTB-XL records. Every prediction is recovered from the device records, intervals resample recordings or patients, and classical baselines are trained on the same units and tested on the same executed inputs. No quantum model reaches its matched support vector machine in ROC-AUC on either primary task, and none is separably better in balanced accuracy. The angle-encoded fidelity and projected kernels reach ROC-AUC 0.697 and 0.693 on apnea, paired differences of $-0.127$ and $-0.119$ to the support vector machine, and both are classical by construction, since the entangling layer of the encoding cancels from the fidelity kernel and leaves closed-form local expectations in the projected kernel. A variational classifier comes closest, reaching 0.797 on apnea (difference $-0.012$, 95% interval $[-0.052, +0.027]$), and falls to chance without its two-qubit gates. Test sets of the size that circuit budgets favour are unreliable, as random 80-minute subsets of the apnea pool give the fidelity kernel a central 95% range of ROC-AUC from 0.589 to 0.797. Readout mitigation changed ROC-AUC by at most 0.015.