@article{pop00094,
	author = {F Scala and A Ceschini and M Panella and ...},
	title = {A general approach to dropout in quantum neural networks},
	journal = {Advanced Quantum …},
	publisher = {Wiley Online Library},
	doi = {10.1002/qute.202300220},
	url = {https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/qute.202300220},
	fulltext = {https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.202300220},
	related = {https://scholar.google.com/scholar?q=related:3xCRjW8UCAYJ:scholar.google.com/\&scioq=%22icsc%22%7Ccn00000013+hpc%7C%22high+performance+computing%22+%22big+data%22+quantum\&hl=en\&as_sdt=0,5},
	year = {2023},
	abstract = {… increasing depth of quantum circuits as … quantum dropout strategies to avoid overfitting and achieve a high level of generalization. This study allows to envision the power of quantum …},
	note = {22 cites: https://scholar.google.com/scholar?cites=434619833390272735\&as_sdt=2005\&sciodt=0,5\&hl=en},
	note = {Query date: 2025-10-13 00:07:46},
}

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