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Intell."],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>An active area of investigation in the search for quantum advantage is quantum machine learning. Quantum machine learning, and parameterized quantum circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find a moderate to strong correlation between the ability of the circuit to uniformly address the Hilbert space and the achieved classification accuracy for circuits that entail a single embedding layer followed by 1 or 2 circuit designs. This is based on our study encompassing 19 circuits in both 1- and 2-layer configurations, evaluated on 9 datasets of increasing difficulty. We also evaluate the correlation between entangling capability and classification accuracy in a similar setup, and find a weak correlation. Future work will focus on evaluating if this holds for different circuit designs.<\/jats:p>","DOI":"10.1007\/s42484-021-00038-w","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T12:02:49Z","timestamp":1615464169000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4376-8568","authenticated-orcid":false,"given":"Thomas","family":"Hubregtsen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josef","family":"Pichlmeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Stecher","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koen","family":"Bertels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"key":"38_CR1","unstructured":"Abadi M (2015) TensorFlow: Large-scale machine learning on heterogeneous systems. http:\/\/tensorflow.org\/, Software available from tensorflow.org"},{"key":"38_CR2","unstructured":"Abraham H (2019) Qiskit: An open-source framework for quantum computing"},{"issue":"7779","key":"38_CR3","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, et al. 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