arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2408.01424quant-ph
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分布式量子计算的广义电路划分

Generalised Circuit Partitioning for Distributed Quantum Computing

Felix Burt, Kuan-Cheng Chen, Kin Leung

AI总结:

本文提出一种基于图的分布式量子计算电路划分方法,联合优化门与态传送成本,利用遗传算法在平均e-bit成本和时间扩展上超越现有方法。

摘要:

分布式量子计算(DQC)是一种旨在通过互连较小的量子处理单元(QPU)来扩展量子计算规模的新范式。共享纠缠允许在QPU之间传送量子态和量子门。这带来了量子处理能力的有吸引力的水平扩展,但代价是纠缠共享协议引入的额外时间和噪声。因此,跨多个QPU划分量子电路的方法应旨在最小化分布式QPU之间所需的基于纠缠的通信量。现有协议往往主要侧重于优化门传送或态传送的纠缠成本,以覆盖QPU之间的操作,而不是同时优化两者。该问题的最一般形式应同等对待门传送和态传送,通过两者的组合实现最小成本的电路划分。本工作引入了一种基于图的公式化方法,允许联合优化门传送和态传送成本,包括门传送的扩展,该扩展将门分组在一起以利用公共资源进行分布。该公式化方法允许各种电路类型实现低e-bit成本。使用基本的遗传算法,在平均e-bit成本和时间扩展方面获得了优于最先进方法的性能。

英文摘要:

Distributed quantum computing (DQC) is a new paradigm aimed at scaling up quantum computing via the interconnection of smaller quantum processing units (QPUs). Shared entanglement allows teleportation of both states and gates between QPUs. This leads to an attractive horizontal scaling of quantum processing power, which comes at the expense of the additional time and noise introduced by entanglement sharing protocols. Consequently, methods for partitioning quantum circuits across multiple QPUs should aim to minimise the amount of entanglement-based communication required between distributed QPUs. Existing protocols tend to focus primarily on optimising entanglement costs for gate teleportation or state teleportation to cover operations between QPUs, rather than both at the same time. The most general form of the problem should treat gate and state teleportation on the same footing, allowing minimal cost circuit partitions through a combination of the two. This work introduces a graph-based formulation which allows joint optimisation of gate and state teleportation cost, including extensions of gate teleportation which group gates together for distribution using common resources. The formulation permits low e-bit cost for a variety of circuit types. Using a basic genetic algorithm, improved performance over state-of-the-art methods is obtained in terms of both average e-bit cost and time scaling.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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