CMU 的量子退火研究:算法、硬件和应用

IF 2.4 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sridhar Tayur, Ananth Tenneti
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引用次数: 0

摘要

在这篇微型综述中,我们介绍并总结了卡内基梅隆大学量子技术组(QTG)与量子退火计算经验相关的研究,这些研究是与包括印度理工学院马德拉斯分校(IIT-Madras)和美国国家航空航天局(NASA)(QuAIL)在内的其他几家机构合作完成的。我们介绍了一种用于非线性整数优化的新型混合量子-古典启发式算法(GAMA,Graver Augmented Multi-seed Algorithm),并在一个应用(癌症基因组学)中进行了说明。然后,我们介绍了一种基于代数几何的算法,该算法可将问题嵌入到未完全连接的硬件上,同时还介绍了一种配套的整数编程(IP)方法。接下来,我们讨论了两种光子设备--时空多路复用伊辛机(TMIM)和空间光子伊辛机(SPIM)--在最大切割和数分实例上的性能。最后,我们将概述当前的工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantum annealing research at CMU: algorithms, hardware, applications
In this mini-review, we introduce and summarize research from the Quantum Technologies Group (QTG) at Carnegie Mellon University related to computational experience with quantum annealing, performed in collaboration with several other institutions including IIT-Madras and NASA (QuAIL). We present a novel hybrid quantum-classical heuristic algorithm (GAMA, Graver Augmented Multi-seed Algorithm) for non-linear, integer optimization, and illustrate it on an application (in cancer genomics). We then present an algebraic geometry-based algorithm for embedding a problem onto a hardware that is not fully connected, along with a companion Integer Programming (IP) approach. Next, we discuss the performance of two photonic devices - the Temporal Multiplexed Ising Machine (TMIM) and the Spatial Photonic Ising Machine (SPIM) - on Max-Cut and Number Partitioning instances. We close with an outline of the current work.
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来源期刊
Frontiers in Computer Science
Frontiers in Computer Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.30
自引率
0.00%
发文量
152
审稿时长
13 weeks
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