重新审视量子电路的映射:进入多核时代

Pau Escofet, Anabel Ovide, Medina Bandic, Luise Prielinger, Hans van Someren, S. Feld, Eduard Alarc'on, S. Abadal, Carmen G. Almud'ever
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引用次数: 0

摘要

量子计算代表着计算模式的转变,为解决经典计算机难以解决的复杂问题提供了可能。尽管目前的量子处理器已经由几百个量子比特组成,但其可扩展性仍然是一个重大挑战。模块化量子计算架构已成为扩大量子计算系统规模的一种有前途的方法。本文深入探讨了分布式多核量子计算的关键问题,重点关注量子电路映射,这是跨核成功执行量子算法同时最大限度减少核间通讯的一项基本任务。我们推导了随机量子电路所需的非本地通信数量的理论边界,并介绍了匈牙利量子比特分配(HQA)算法,这是一种多核映射算法,旨在优化内核的量子比特分配,从而减少内核间通信。我们针对模块化架构的最先进电路映射算法对 HQA 进行了详尽的评估,结果显示,与性能最好的算法相比,HQA 在执行时间和非本地通信方面分别提高了 4.9 倍和 1.6 倍。HQA 是将量子电路映射到多核架构中的一种非常有前途的可扩展方法,是大规模利用量子计算潜力的重要工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era
Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred of qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This paper delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a 4.9 × and 1.6 × improvement in terms of execution time and non-local communications, respectively, compared to the best performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.
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