A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
Quantum circuits using FRQI and QPIE encodings implement Sobel edge and Harris corner detection, yielding outputs consistent with classical methods in noiseless simulations, with QPIE showing greater stability under limited shots.
citing papers explorer
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
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Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels
Quantum circuits using FRQI and QPIE encodings implement Sobel edge and Harris corner detection, yielding outputs consistent with classical methods in noiseless simulations, with QPIE showing greater stability under limited shots.