发表机构
IBM Research; Trinity Business School; Donostia International Physics Center (DIPC); Nanoscale Device Characterization Division, National Institute of Standards and Technology; Centro de Física de Materiales CFM/MPC (CSIC-UPV/EHU); Advanced Micro Devices Inc.（IBM 研究部; 三一商学院; 圣塞巴斯蒂安国际物理中心; 美国国家标准与技术研究院纳米器件表征部门; 材料物理中心 (西班牙国家科研委员会-巴斯克国立大学/阿尔贝塔理工大学); 超威半导体公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出量子误差 mitigation 技术林德布拉德多乘积公式，融入Clifford重标度技术，在65量子比特量子计算机应用获最高5.6倍GPU加速。
AI 中文摘要
我们提出了林德布拉德多乘积公式：一种量子误差 mitigation 技术，采用二维张量网络与经环路修正的置信传播收缩。用张量网络计算该误差 mitigation 方案所需的量，其计算成本可能低于直接计算期望值，从而使我们的方法可应用于某些系统，而这些系统的张量网络方法可能难以计算目标可观测量。该工作流程融入了 Clifford 重标度技术，并输出估计误差棒。我们将该方法应用于先前研究的二维离散时间晶体模型，在量子计算机 ibm_basquecountry 上实现了排列成 3×3 重六边形拓扑的 65 量子比特。结果显示，我们工作流程经典部分的 GPU 实现获得了最高达 5.6 倍的加速。
英文摘要
We introduce Lindblad Multiproduct Formulas: a quantum error mitigation technique that uses two-dimensional tensor networks contracted with loop-corrected belief propagation. The quantities required to implement the error mitigation scheme that are evaluated with tensor networks can be less computationally expensive to calculate than the expectation values themselves, thus allowing for the possibility of applying our method to certain systems for which tensor network methods may struggle to calculate the observable quantities of interest. The workflow incorporates Clifford rescaling techniques and outputs an estimated error bar. We apply our method to a model of two-dimensional discrete time crystals studied previously and implement it on $65$ qubits arranged in a $3\!\times\!3$ heavy-hexagonal topology on the quantum computer ibm_basquecountry. We show that a GPU implementation of the classical part of our workflow achieves a speedup of up to $5.6\times$.
