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About this paper
Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola, Filippo Caruso
The authors test a hybrid classifier for multiclass photovoltaic faults that first compresses sensor sequences with a classical Transformer autoencoder and then uses a quantum-containing classification layer. On this dataset, it reports balanced accuracy of 0.9761, F1 0.9418, and 109 parameters, compared with 0.9549, 0.9005, and approximately 4,400 for its classical comparator. Results are limited to this split-based experiment.
Summary by alphaXiv.
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