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The classical backbone uses a pretrained with bidirectional Long Short-Term Memory layers; the quantum component is a 4-qubit, shallow-depth circuit whose input angles are modulated by a dynamically computed error-mitigation factor derived from the gradient of an effective potential. We perform a grid search over three hardware-relevant parameters: a rescaling factor\n \n \n $$R$$<\/jats:tex-math>\n \n R<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n (an amplitude\/drive scaler in our surrogate) that modulates the effective potential and controls the input rotation amplitude, a secondary coupling strength\n \n \n $$\\lambda $$<\/jats:tex-math>\n \n \u03bb<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , and the bare Josephson energy\n \n \n $$E_{J0}$$<\/jats:tex-math>\n \n \n E<\/mml:mi>\n \n J<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n . In a 10-class subset experiment (top-dose patients), the hybrid model exhibits a performance maximum when\n \n \n $$R=0.95$$<\/jats:tex-math>\n \n \n R<\/mml:mi>\n =<\/mml:mo>\n 0.95<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , reaching a test accuracy of up to 0.95 across several\n \n \n $$(\\lambda , E_{J0})$$<\/jats:tex-math>\n \n \n (<\/mml:mo>\n \u03bb<\/mml:mi>\n ,<\/mml:mo>\n \n E<\/mml:mi>\n \n J<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n )<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n settings. In this work, deviations of\n \n \n $$R$$<\/jats:tex-math>\n \n R<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n $$\\lambda $$<\/jats:tex-math>\n \n \u03bb<\/mml:mi>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , and\n \n \n $$E_{J0}$$<\/jats:tex-math>\n \n \n E<\/mml:mi>\n \n J<\/mml:mi>\n 0<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n are treated as quasi-static hardware imperfections (systematic calibration offsets and slow drift) rather than as stochastic noise channels. We interpret this optimum as a balance between expressivity and over-rotation\/leakage in our simulator when the effective drive is slightly reduced. To benchmark consistency on the full dataset, we additionally report a window-level evaluation over all 40 patients with the classical backbone alone: a single 80\/10\/10 split yields\n \n \n $$\\text {train}=0.959$$<\/jats:tex-math>\n \n \n train<\/mml:mtext>\n =<\/mml:mo>\n 0.959<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n $$\\text {val}=0.960$$<\/jats:tex-math>\n \n \n val<\/mml:mtext>\n =<\/mml:mo>\n 0.960<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n $$\\text {test}=0.909$$<\/jats:tex-math>\n \n \n test<\/mml:mtext>\n =<\/mml:mo>\n 0.909<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n accuracy (windows); stratified threefold cross-validation achieves\n \n \n $$0.876 \\pm 0.005$$<\/jats:tex-math>\n \n \n 0.876<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.005<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n mean validation accuracy. Overall, our results indicate that such hardware-imperfection-aware hybrid models can be competitive with strong classical baselines while offering a physics-grounded knob for hardware-aware calibration; we discuss modeling assumptions and limitations (e.g., simplified hardware-imperfection model, connectivity, and task definition) to avoid overclaiming clinical readiness.\n <\/jats:p>","DOI":"10.1007\/s11128-026-05144-x","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:18:09Z","timestamp":1775639889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Squid\u2013transmon quantum hardware simulation with deep learning for pancreatic radiotherapy image classification"],"prefix":"10.1007","volume":"25","author":[{"given":"Javier","family":"Villalba-D\u00edez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Gonz\u00e1lez-Marcos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Carlos","family":"Losada-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joaqu\u00edn","family":"Ordieres-Mer\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"5144_CR1","doi-asserted-by":"publisher","first-page":"57974","DOI":"10.1109\/ACCESS.2024.3388005","volume":"12","author":"P Singh","year":"2024","unstructured":"Singh, P., Dasgupta, R., Singh, A., Pandey, H., Hassija, V., Chamola, V., Sikdar, B.: A survey on available tools and technologies enabling quantum computing. 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