Key result
Quantum kernel model predicts pre-hypotensive states from PPG and ECG with ~62% balanced accuracy.
Why the study?
Full quantum-kernel estimation requires a quadratic number of pairwise fidelity evaluations, limiting hardware studies on public cloud QPUs for pre-hypotensive PPG and ECG data.
Does the Hemodynamic Transition Quantum Kernel accurately predict pre-hypotensive states using PPG and ECG features?
Does the Hemodynamic Transition Quantum Kernel accurately predict pre-hypotensive states using PPG and ECG features?
Effect estimate: balanced accuracy 0.623 (95% CI 0.576-0.674)
A transition-aware quantum kernel can predict pre-hypotensive states from PPG and ECG data on physical quantum hardware with high alignment to the exact mathematical kernel.
Quantum kernels for pre-hypotension detection should not yet inform practice; leaves open their perioperative biosignal utility pending prospective validation.
Full quantum-kernel estimation requires a quadratic number of pairwise fidelity evaluations, which limits hardware studies on public cloud QPUs. We constructed paired stable and pre-hypotensive observations from VitalDB and represented each observation by its current physiological state and the change across two adjacent 60-s windows. Development-only analysis selected a 30-s prediction gap and four PPG/ECG-derived features: PPG amplitude, PPG peak width at half prominence, pulse interval, and pulse-arrival time. The Hemodynamic Transition Quantum Kernel (HTQK) encodes four state values with R y rotations and their four corresponding changes with R z rotations on the same four qubits, separated by an internal nearest-neighbour CX bridge. This placement avoids the cancellation that affected a fixed terminal entangler in the original compute–uncompute circuit. The locked kernel was then validated through exact, repeated finite-shot, backend-derived-noise, and physical-QPU stages. Physical validation was restricted to a deterministically selected subset of 16 development and eight holdout observations. The entangled kernel achieved holdout balanced accuracy of 0.623 (95% bootstrap CI 0.576–0.674). On ibm_marrakesh , 252 circuits used 148 QPU s. Relative Frobenius distortion from the exact training kernel was 0.0420 and centred kernel alignment was 0.9992. The method evaluates resource-efficient kernel preservation rather than quantum computational advantage.
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Darnapuk et al. (2026) studied Pre-hypotension. Hemodynamic Transition Quantum Kernel (HTQK) was evaluated on Holdout balanced accuracy (balanced accuracy 0.623, 95% CI 0.576-0.674). The Hemodynamic Transition Quantum Kernel achieved a holdout balanced accuracy of 0.623 (95% CI 0.576-0.674) for predicting pre-hypotensive states using PPG and ECG features.
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