{"paper":{"title":"Optimal Quantum Sample Complexity of Learning Algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CC","cs.LG"],"primary_cat":"quant-ph","authors_text":"Ronald de Wolf (CWI, Srinivasan Arunachalam (CWI), U of Amsterdam)","submitted_at":"2016-07-04T15:31:32Z","abstract_excerpt":"$ \\newcommand{\\eps}{\\varepsilon} $In learning theory, the VC dimension of a concept class $C$ is the most common way to measure its \"richness.\" In the PAC model $$ \\Theta\\Big(\\frac{d}{\\eps} + \\frac{\\log(1/\\delta)}{\\eps}\\Big) $$ examples are necessary and sufficient for a learner to output, with probability $1-\\delta$, a hypothesis $h$ that is $\\eps$-close to the target concept $c$. In the related agnostic model, where the samples need not come from a $c\\in C$, we know that $$ \\Theta\\Big(\\frac{d}{\\eps^2} + \\frac{\\log(1/\\delta)}{\\eps^2}\\Big) $$ examples are necessary and sufficient to output an "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.00932","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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"}