Adaptive job and resource management for the growing quantum cloud
Adaptive job and resource management for the growing quantum cloud
复制标题
针对不断增长的量子云的自适应作业和资源管理
DOI：
10.1109/qce52317.2021.00047
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Chong, Frederic T.
中科院分区：
文献类型：
--
作者：
Ravi, Gokul Subramanian;Smith, Kaitlin N.;Murali, Prakash;Chong, Frederic T.
随着量子计算的不断普及，通过云进行高效的量子机器访问对于全球学术和行业研究人员来说都至关重要地球仪。随着云量子计算需求呈指数级增长，对资源消耗和执行特性的分析是供应商端和客户端有效管理作业和资源的关键。虽然作业/资源消耗和管理的分析和优化在经典HPC领域很受欢迎，但对于量子计算等新兴技术来说，这是严重缺乏的。本文提出了优化的量子云自适应作业调度，注意到机器之间的排队时间和保真度趋势等主要特征，以及诸如服务质量保证和机器校准约束的其它特性。该提案的关键组成部分包括a）一个预测模型，该模型基于编译的电路特征（如电路深度和不同形式的错误）预测机器上的保真度趋势，以及B）基于执行时间估计的每台机器的排队时间预测。总的来说，该提案在不同的量子应用和系统加载场景中的模拟IBM机器上进行了评估，并且与传统的任务分配器相比，能够将等待时间减少3倍以上，并在特定使用情况下将保真度提高40%以上。
As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations.Overall, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.
影响因子：
6.4
作者：
Preskill, John
通讯作者：
Preskill, John
阿贡领导型超级计算机批量调度的经验与实践
DOI：
10.1007/978-3-319-77398-8_1
发表时间：
2017
期刊：
2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子：
--
作者：
W. Allcock;Paul M. Rich;Yuping Fan;Z. Lan
通讯作者：
Z. Lan