截断混合张量网络用于分布式量子模拟
Truncated hybrid tensor networks for distributed quantum simulation
- National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出截断混合张量网络（THTN）框架，通过经典连接张量合并远程门并截断施密特模式，将分布式量子模拟映射到一维切割，降低采样成本，并在链、梯子和分层模型上验证了与TEBD一致的精度。
中文摘要 AI 辅助
量子多体系统的模拟是量子计算的主要应用之一，但现有设备受限于量子比特数量，无法容纳所需规模的系统。一种方法是将大规模演化分布在多个仅交换经典信息的适度规模的分布式系统上。当前协议如电路拼接（circuit knitting）会引入准概率采样成本，该成本随切割处远程门数量的增加呈指数增长，即使切割处实际的物理关联是有界的。在本工作中，我们提出了一种截断混合张量网络（THTN）框架，其中远程两体门被实现为子系统上局部酉算子的和，通过经典连接张量相连，并且界面截断保留了跨越切割的施密特模式。同一子系统之间的远程门可以合并到一个连接器上，因此非一维模型，例如具有强层内和较弱层间耦合的分层分区，可以被映射到一维切割上。保留的非负施密特系数也为局部可观测量定义了采样规则。我们通过经典模拟与相同键维下的时间演化块消去（TEBD）对比，验证了该分布式协议。截断动力学在链和梯子上追踪了TEBD参考结果，在分层模型上获得了最明显的增益。
英文摘要
Simulation of quantum many-body systems is a principal application of quantum computing, but available devices remain limited by the number of qubits and cannot accommodate systems of the desired size. One approach is to host a large evolution across several modest distributed systems that exchange classical information alone. Current protocols such as circuit knitting incur a quasi-probability sampling cost that grows exponentially with the number of remote gates across the cut, even when physical correlations across the cut are actually bounded. In this work, we present a truncated hybrid tensor network (THTN) framework in which a remote two-body gate is applied as a sum of local unitaries on the subsystems, linked by a classical connecting tensor, and an interface truncation retains Schmidt modes across the cut. Remote gates between the same subsystems can be merged on one connector, so non-one-dimensional models, such as layered partitions with strong intra-layer and weaker interlayer couplings, may be cast onto a one-dimensional cut. The retained non-negative Schmidt coefficients also define a sampling rule for local observables. We validate this distributed protocol by classical simulation against time-evolving block decimation (TEBD) at the same bond dimension. The truncated dynamics track the TEBD references on chains and ladders, with the clearest gain on a layered model.
