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.
The bitter truth about gate-based quantum algorithms in the nisq era,
2 Pith papers cite this work. Polarity classification is still indexing.
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Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.
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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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Profiling the Effective Limits of Error Mitigation via Circuit Replication
Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.