恒定每层深度的MPS预训练拟设用于噪声分布式量子处理器
Constant-Per-Layer-Depth MPS-Pretrained Ansatz for Noisy Distributed Quantum Processors
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中文总结 AI 辅助
本研究提出恒定每层深度的MPS预训练拟设，通过硬件感知协同设计（电路调度与通信拓扑），在分布式量子处理器上实现噪声下性能优于或匹配单处理器砖墙架构的变分量子计算。
中文摘要 AI 辅助
分布式量子处理器可以将变分算法扩展到单个设备之外，但电路深度、通信开销和噪声限制了其性能。我们比较了矩阵乘积态（MPS）预训练的阶梯形、混合正则形和砖墙形实现，并匹配了每层双量子比特块资源。尽管理想变分量子本征求解器性能和梯度尺度相当，但较浅的砖墙架构减少了电路持续时间、空闲时间退相干和零噪声外推（ZNE）开销，从而在噪声和ZNE辅助下表现出更优性能。随后，我们将MPS预训练电路扩展到每个量子处理单元（QPU）具有一个通信量子比特的模块化处理器；一种最近邻QPU路径调度使得随着QPU的添加，每层深度保持恒定。当通信空闲时间远短于相干时间时，其互连QPU链路实现目标哈密顿量长程相互作用的分布式电路在噪声下匹配或优于单处理器砖墙架构。这些结果确立了张量网络预训练、电路调度和通信拓扑的硬件感知协同设计作为噪声模块化硬件上变分量子计算的原则。
英文摘要
Distributed quantum processors could scale variational algorithms beyond single devices, but circuit depth, communication overhead, and noise limit their performance. We compare ladder, mixed-canonical, and brick-wall realizations of matrix-product-state (MPS) pretraining with matched per-layer two-qubit-block resources. Despite comparable ideal variational quantum eigensolver performance and gradient scales, the shallower brick-wall architecture reduces circuit duration, idle-time decoherence, and zero-noise-extrapolation (ZNE) overhead, yielding superior noisy and ZNE-assisted performance. We then extend MPS-pretrained circuits to modular processors with one communication qubit per quantum processing unit (QPU); a nearest-neighbor QPU-path schedule keeps the per-layer depth constant as QPUs are added. Distributed circuits whose inter-QPU links realize long-range interactions of the target Hamiltonian match or outperform the single-processor brick-wall under noise when communication idle time is short compared with the coherence time. These results establish hardware-aware co-design of tensor-network pretraining, circuit scheduling, and communication topology as a principle for variational quantum computation on noisy modular hardware.
发表机构
- Korea Institute of Science and Technology（韩国科学技术院）
- xDots
- Seoul National University（首尔大学）
- Korea University of Science and Technology（韩国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。
