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dc.contributor.authorDarnapuk, Y.-
dc.contributor.authorKravchenko, P.-
dc.contributor.authorChuiko, G.-
dc.date.accessioned2026-10-26T08:58:28Z-
dc.date.available2026-10-26T08:58:28Z-
dc.date.issued2026-
dc.identifier.issn22150161-
dc.identifier.urihttps://www.scopus.com/pages/publications/105053041266?discoveryEventID=f4b41bff-7da9-4f69-b528-aab8a6bfd168&alertType=scopusaffil&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_ru_ru_email-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2215016126004085-
dc.identifier.urihttps://dspace.chmnu.edu.ua/jspui/handle/123456789/3400-
dc.descriptionDarnapuk, Y., Kravchenko, P., & Chuiko, G. (2026). Construction and hardware validation of a transition-aware quantum kernel for pre-hypotensive PPG and ECG. MethodsX, 17, art. no. 104192. DOI : 10.1016/j.mex.2026.104192uk_UA
dc.description.abstractFull 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 Ry[jls-end-space/]rotations and their four corresponding changes with Rz[jls-end-space/]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. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/uk_UA
dc.language.isoenuk_UA
dc.publisherElsevier B.V.uk_UA
dc.subjectQuantum kerneluk_UA
dc.subjectQuantum machine learninguk_UA
dc.subjectPhotoplethysmographyuk_UA
dc.subjectElectrocardiographyuk_UA
dc.subjectIntraoperative hypotensionuk_UA
dc.subjectQuantum hardwareuk_UA
dc.titleConstruction and hardware validation of a transition-aware quantum kernel for pre-hypotensive PPG and ECGuk_UA
dc.typeArticleuk_UA
Appears in Collections:Публікації науково-педагогічних працівників ЧНУ імені Петра Могили у БД Scopus

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