Squid–transmon quantum hardware simulation with deep learning for pancreatic radiotherapy image classification
Abstract
We present a hybrid quantum–classical framework that couples a parameterized, hardware-imperfection-aware SQUID–Transmon surrogate simulation with deep learning to classify pancreatic radiotherapy CT/CBCT images from the Pancreatic-CT-CBCT-SEG collection. The classical backbone uses a pretrained EfficientNetB0 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 R\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R$$\end{document} (an amplitude/drive scaler in our surrogate) that modulates the effective potential and controls the input rotation amplitude, a secondary coupling strength λ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda $$\end{document}, and the bare Josephson energy EJ0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs}…
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