{"paper":{"title":"Large-scale quantum reservoir learning with an analog quantum computer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","physics.atom-ph"],"primary_cat":"quant-ph","authors_text":"Adam Choukri, Alexander Keesling, Alexander Lukin, Alexei Bylinskii, Andrii Zhdanov, Anshuman Singh, Chen Zhao, Daniel Waxman-Lenz, David Haines, David Paquette, Evgeny Ostroumov, Fangli Liu, Florian Huber, Hengyun Zhou, Henry Thoreen, Hong-Ye Hu, James I. Basham, John Long, John Robinson, Jonathan Lopatin, Jonathan Wurtz, Joseph Campo, Julian Hammett, Kai-Hsin Wu, Kevin Bagnall, Luis A. Mart\\'inez-Mart\\'inez, Majd Hamdan, Milan Kornja\\v{c}a, Ming-Guang Hu, Minho Kwon, Nandan Sinha, Nathan Gemelke, Ning Hsu, Ningyuan Jia, Noel Wan, Ognjen Markovi\\'c, Paige Frederick, Paul Niklas Jepsen, Pedro L. S. Lopes, Pedro Sales Rodriguez, Phillip Weinberg, Robert DeAngelo, Rodrigo Araiza Bravo, Sergio H. Cantu, Sheng-Tao Wang, Susanne F. Yelin, Takuya Kitagawa, Tak Wong, Thomas Karolyshyn, Tommaso Macr\\`i, Xianmei Meng, Xun Gao, Yuval Boger","submitted_at":"2024-07-02T18:00:00Z","abstract_excerpt":"Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02553","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.02553/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}