Architectural Robustness to Label Noise Using Variational Quantum Circuits for Medical Image Classification

arXiv:2610.16153 · quant-ph · Submitted 2026-10-12 · Read on arXiv

quant-ph

Submitted: 2026-10-12

Updated: 2026-10-12

Comments: Accepted at the Second AAAI Symposium on Quantum Information & Machine Learning (QIML), AAAI 2026 Fall Symposium Series. 8 pages, 1 figure

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantum Circuits — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes

Sources

• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
• PennyLane-Lightning MPI: A massively scalable quantum circuit simulator based on distributed computing in CPU clusters
• Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook
• A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

Related papers

• Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
• Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
• Polynomial-time classical and quantum simulation of quantum impurity models
• Incoherent Imaging with Spatially Structured Quantum Probes
• A quasiparticle-protected superconducting qubit
• Theory of quantum-enhanced interferometry with general Markovian light sources