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Our results indicate that, under ideal or low-noise conditions and particularly for small-scale problems, ans\u00e4tze with high Hamiltonian expressibility yield better solution quality for problems with non-diagonal Hamiltonians and superposition state solutions. Conversely, circuits with low expressibility are more effective for problems whose solutions are basis states, including those defined by diagonal Hamiltonians. Under noisy conditions, low-expressibility circuits remain preferable for problems with solutions in a computational basis state, while intermediate expressibility yields better results for some problems involving superposition state solutions.<\/jats:p>","DOI":"10.1007\/s42484-026-00407-3","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T10:11:13Z","timestamp":1783073473000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hamiltonian expressibility for ansatz selection in variational quantum algorithms"],"prefix":"10.1007","volume":"8","author":[{"given":"Filippo","family":"Brozzi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gloria","family":"Turati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Ferrari Dacrema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Filippo","family":"Caruso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Cremonesi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"issue":"5","key":"407_CR1","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s11227-025-07047-7","volume":"81","author":"M AbuGhanem","year":"2025","unstructured":"AbuGhanem M (2025) Ibm quantum computers: evolution, performance, and future directions. 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