A circuit framework fractionalizes dyadic-order unitary operators via ancilla QFT and phase modulation, yielding explicit constructions for the quantum fractional Hartley transform and cosine-transform families of Types I and IV.
A flexible representation of quantum images for polynomial preparation, image compression, and processing operations
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
citing papers explorer
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Dyadic-Order Quantum Fractional Transforms: Circuit Constructions and Applications to Hartley and Cosine Transform Families
A circuit framework fractionalizes dyadic-order unitary operators via ancilla QFT and phase modulation, yielding explicit constructions for the quantum fractional Hartley transform and cosine-transform families of Types I and IV.
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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.