CAREER: The Research of Noise-Aware Scheduling for Noisy Intermediate-Scale Quantum Systems

在噪声中尺度量子（NISQ）时代，提高量子计算的保真度，使量子算法的正确性变得至关重要。量子算法的设计者需要清楚地了解不同量子计算机的隐藏噪声，以及特定量子算法在同一量子计算机上不同编译电路内部的噪声。然而，这不是一项微不足道的任务。社区缺乏系统的解决方案来克服噪音对工作执行的影响。现有的错误缓解方法遭受负担不起的开销或不可预测的性能。所提出的方法解决了来自量子计算机的复杂和动态演变的质量以及编译电路的显著变化的主要挑战。该项目的影响包括为量子云系统设计一个新的噪声感知调度系统，以及为量子云系统测试调度算法的模拟框架。这项研究将提高NISQ时代量子云系统的利用率和效率。所提出的云系统仿真工具包通过启用新的资源管理和调度方法的仿真平台来弥补量子云系统研究缺乏实验平台的差距。此外，课程开发和学生培训计划支持量子信息和科学的多样化和竞争力的劳动力的发展。将开发量子计算的新课程和新课程浓度。研究生和本科生，特别是来自代表性不足的群体，将接受多学科的培训，使他们能够在量子信息和科学领域取得成功。该项目建立在现有知识的基础上，创造一种新的有洞察力的方法，使噪声量子计算机的有效使用成为可能。本项目体现了研究的新奇、实用价值和领域影响。研究团队将设计、开发和应用新的方法，1）对硬件保真度的时空模式进行剖面和建模，研究基于时间模式的时空噪声模型，并进一步构建保真度预测模式; 2）构建了一种新型的噪声感知调度系统，提出了基于电路噪声容限因子和硬件预测的后端分配和量子比特分配的优化调度方法忠诚; 3）构建量子云仿真工具包，让量子系统工程师和研究人员测试和评估新的资源管理和调度策略; 4）建立一个新的培训计划“全民量子计算”，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

In the Noisy Intermediate-Scale Quantum (NISQ) era, it is crucial to improve the fidelity of quantum computing, enabling the correctness of quantum algorithms. Designers of quantum algorithms need to clearly understand the hidden noise of different quantum computers and the noise inside different compiled circuits on the same quantum computer for a specific quantum algorithm. However, this is not a trivial task. The community lacks a systematic solution to overcome the impact of noise on the execution of the jobs. Existing error mitigation methods suffer from unaffordable overhead or unpredictable performance. The proposed approach addresses the major challenges that come from the complex and dynamically evolving quality of quantum computers and significant variations of the compiled circuits. The project's impacts include designing a novel noise-aware scheduling system for quantum cloud systems and a simulation framework for testing scheduling algorithms for quantum cloud systems. The proposed research will enhance the utilization and efficiency of the NISQ-era quantum cloud system. The proposed cloud system simulation toolkit bridges the gap of the lack of an experimental platform for quantum cloud system research by enabling a simulation platform for new resource management and scheduling approaches. In addition, the curriculum development and the student training program support the development of a diverse and competitive workforce of quantum information and science. New courses and new curriculum concentrations in quantum computing will be developed. Graduate and undergraduate students, especially from underrepresented groups, will be trained in multiple disciplines that will enable them to have successful careers in quantum information and science.This project builds upon existing knowledge to create a new insightful approach that enables the efficient usage of noisy quantum computers. This project presents the novelty of the research, practical value, and domain impacts. The research team will design, develop, and apply new methods to 1) profile and model hardware fidelity's temporal and spatial pattern, study the spatial and temporal noise model based on the temporal pattern, and further build the fidelity forecast mode; 2) build a novel noise-aware scheduling system, which proposes the optimal scheduling approach for backend allocation and qubit allocation based on the circuit noise tolerance factor and the prediction of the hardware fidelity; 3) build quantum cloud simulation toolkit to allow the quantum system engineers and researchers to test and evaluate the new resource management and scheduling strategies; 4) build a new training program “Quantum Computing for All”, by compiling the research outcomes to foster the workforce of quantum information and science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.