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These properties make our algorithm preferable for execution on noisy intermediate-scale quantum devices. We demonstrate our approach on an image-classification task on handwritten digits, and show that layerwise learning attains an 8% lower generalization error on average in comparison to standard learning schemes for training quantum circuits of the same size. Additionally, the percentage of runs that reach lower test errors is up to 40% larger compared to training the full circuit, which is susceptible to creeping onto a plateau during training.<\/jats:p>","DOI":"10.1007\/s42484-020-00036-4","type":"journal-article","created":{"date-parts":[[2021,1,25]],"date-time":"2021-01-25T19:05:01Z","timestamp":1611601501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":248,"title":["Layerwise learning for quantum neural networks"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2369-6314","authenticated-orcid":false,"given":"Andrea","family":"Skolik","sequence":"first","affiliation":[]},{"given":"Jarrod R.","family":"McClean","sequence":"additional","affiliation":[]},{"given":"Masoud","family":"Mohseni","sequence":"additional","affiliation":[]},{"given":"Patrick","family":"van der Smagt","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Leib","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,25]]},"reference":[{"issue":"7779","key":"36_CR1","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1038\/s41586-019-1666-5","volume":"574","author":"F Arute","year":"2019","unstructured":"Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, Biswas R, Boixo S, Brandao Fernando GSL, Buell DA et al (2019) Nature 574(7779):505\u2013510","journal-title":"Nature"},{"issue":"1","key":"36_CR2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s41534-019-0157-8","volume":"5","author":"M Benedetti","year":"2019","unstructured":"Benedetti M, Garcia-Pintos D, Perdomo O, Leyton-Ortega V, Nam Y, Perdomo-Ortiz A (2019a) npj Quantum Inf 5(1):45. https:\/\/doi.org\/10.1038\/s41534-019-0157-8, http:\/\/www.nature.com\/articles\/s41534-019-0157-8","journal-title":"npj Quantum Inf"},{"issue":"4","key":"36_CR3","doi-asserted-by":"publisher","first-page":"043023","DOI":"10.1088\/1367-2630\/ab14b5","volume":"21","author":"M Benedetti","year":"2019","unstructured":"Benedetti M, Grant E, Wossnig L, Severini S (2019b) New J Phys 21(4):043023. https:\/\/doi.org\/10.1088\/1367-2630\/ab14b5, http:\/\/stacks.iop.org\/1367-2630\/21\/i=4\/a=043023?key=crossref.0b5ab94ed3e2ea2943830f1d0073c780","journal-title":"New J Phys"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Bengio Y, Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. 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