Abstract
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. The proposed methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling (Twir), and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.
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Data availability
The MedMNIST datasets used in this benchmarking study can be accessed using https://medmnist.com/.
Code availability
All the related codes are available at https://github.com/gurinder-hub/QML_MedMNIST.
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Acknowledgements
We extend our sincere gratitude to Danil Kaliakin (Research Associate, Cleveland Clinic) and Abdullah Ash Saki (Researcher at IBM Quantum) for their valuable insights and support throughout this project. We are also grateful to the authors of Élivágar and Torchquantum for their great work and open-sourcing the code.
Funding
The research is funded by the NIH (GM130641).
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G. Singh and H. Jin proposed and designed the study of benchmarking MedMNIST datasets using quantum machine learning on real IBM Cleveland hardware. G. Singh and H. Jin performed the experiments under the supervision of K.M. Merz. G. Singh wrote the manuscript, with inputs and contributions from all authors.
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Singh, G., Jin, H. & Merz Jr., K.M. Benchmarking MedMNIST dataset on real quantum hardware. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35605-3
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DOI: https://doi.org/10.1038/s41598-026-35605-3
