{"ID":2843960,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07092","arxiv_id":"2511.07092","title":"Sample-efficient quantum error mitigation via classical learning surrogates","abstract":"The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal. However, QEM techniques incur substantial measurement overheads, especially when applied to families of quantum circuits parameterized by classical inputs. Focusing on zero-noise extrapolation (ZNE), a widely adopted QEM technique, here we devise the surrogate-enabled ZNE (S-ZNE), which leverages classical learning surrogates to perform ZNE entirely on the classical side. Unlike conventional ZNE, whose measurement cost scales linearly with the number of circuits, S-ZNE requires only constant measurement overhead for an entire family of quantum circuits, offering superior scalability. Theoretical analysis indicates that S-ZNE achieves accuracy comparable to conventional ZNE in many practical scenarios, and numerical experiments on up to 100-qubit ground-state energy and quantum metrology tasks confirm its effectiveness. Our approach provides a template that can be effectively extended to other quantum error mitigation protocols, opening a promising path toward scalable error mitigation.","short_abstract":"The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal....","url_abs":"https://arxiv.org/abs/2511.07092","url_pdf":"https://arxiv.org/pdf/2511.07092v1","authors":"[\"Wei-You Liao\",\"Ge Yan\",\"Yujin Song\",\"Tian-Ci Tian\",\"Wei-Ming Zhu\",\"De-Tao Jiang\",\"Yuxuan Du\",\"He-Liang Huang\"]","published":"2025-11-10T13:29:29Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}