{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:36:45Z","timestamp":1779251805523,"version":"3.51.4"},"reference-count":55,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T00:00:00Z","timestamp":1685145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NCCR MARVEL, a National Centre of Competence in Research","award":["205602"],"award-info":[{"award-number":["205602"]}]},{"name":"Swiss National Science Foundation","award":["205602"],"award-info":[{"award-number":["205602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The discovery of quantum algorithms offering provable advantages over the best known classical alternatives, together with the parallel ongoing revolution brought about by classical artificial intelligence, motivates a search for applications of quantum information processing methods to machine learning. Among several proposals in this domain, quantum kernel methods have emerged as particularly promising candidates. However, while some rigorous speedups on certain highly specific problems have been formally proven, only empirical proof-of-principle results have been reported so far for real-world datasets. Moreover, no systematic procedure is known, in general, to fine tune and optimize the performances of kernel-based quantum classification algorithms. At the same time, certain limitations such as kernel concentration effects\u2014hindering the trainability of quantum classifiers\u2014have also been recently pointed out. In this work, we propose several general-purpose optimization methods and best practices designed to enhance the practical usefulness of fidelity-based quantum classification algorithms. Specifically, we first describe a data pre-processing strategy that, by preserving the relevant relationships between data points when processed through quantum feature maps, substantially alleviates the effect of kernel concentration on structured datasets. We also introduce a classical post-processing method that, based on standard fidelity measures estimated on a quantum processor, yields non-linear decision boundaries in the feature Hilbert space, thus achieving the quantum counterpart of the radial basis functions technique that is widely employed in classical kernel methods. Finally, we apply the so-called quantum metric learning protocol to engineer and adjust trainable quantum embeddings, demonstrating substantial performance improvements on several paradigmatic real-world classification tasks.<\/jats:p>","DOI":"10.3390\/e25060860","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T16:18:43Z","timestamp":1685204323000},"page":"860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimizing Quantum Classification Algorithms on Classical Benchmark Datasets"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1446-5502","authenticated-orcid":false,"given":"Manuel","family":"John","sequence":"first","affiliation":[{"name":"IBM Quantum, IBM Research Europe\u2014Zurich, 8803 R\u00fcschlikon, Switzerland"},{"name":"Institute for Theoretical Physics, ETH Z\u00fcrich, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7011-6477","authenticated-orcid":false,"given":"Julian","family":"Schuhmacher","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research Europe\u2014Zurich, 8803 R\u00fcschlikon, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9428-913X","authenticated-orcid":false,"given":"Panagiotis","family":"Barkoutsos","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research Europe\u2014Zurich, 8803 R\u00fcschlikon, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5690-1981","authenticated-orcid":false,"given":"Ivano","family":"Tavernelli","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research Europe\u2014Zurich, 8803 R\u00fcschlikon, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2008-5956","authenticated-orcid":false,"given":"Francesco","family":"Tacchino","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research Europe\u2014Zurich, 8803 R\u00fcschlikon, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1038\/s43588-022-00311-3","article-title":"Challenges and opportunities in quantum machine learning","volume":"2","author":"Cerezo","year":"2022","journal-title":"Nat. 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