Collaborative Research: OAC Core: An Integrated Framework for Enabling Temporal-Reliable Quantum Learning on NISQ-era Devices

合作研究：OAC Core：在 NISQ 时代设备上实现时间可靠的量子学习的集成框架

• 批准号: 2311949
• 负责人: Weiwen Jiang
• 金额: $ 33万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311949&HistoricalAwards=false
• 关键词: Collaborative; Research; OAC; Core; Integrated

As quantum computers consistently scale up with more qubits, the development of practical and real-world applications using quantum computing has become a crucial frontier for quantum information scientists and technologists, which benefits other scientists and end-users across a wide range of disciplines. Quantum learning, a combination of quantum computing and machine learning and also known as Variational Quantum Algorithm (VQA), is one of the most promising approaches to be applied to a variety of practical problems. Quantum learning is a hybrid quantum-classical protocol that optimizes parameters in a Variational Quantum Circuit (VQC) with a cost function using a classical training optimizer. However, the inherent noise on quantum devices brings severe deployability and portability issues, making the optimized VQCs suffer significant performance degradation in deploying or porting among different quantum computers. What is more, the noise on the quantum devices changes over time, known as unstable noise, fluctuating noise, or drift of noise, which prevents the reuse of VQCs on one quantum computer at different times and even misleads the learning to a non-optimal path when noise change during the VQC training process. This project aims to enable temporal-reliable quantum learning by generating fundamental understandings and practical approaches in quantum learning, uncertainty prediction, noise suppression, and system visualization. Outcomes are evaluated using quantum learning for scientific applications on the DoE-sponsored supercomputing centers that provide access to various commercial quantum computing resources. With the objective of facilitating practical quantum learning, this project uses a systematic and innovative approach to develop an integrated framework, which presents the novelty of the proposed research, practical value, and domain impacts: (1) developing a novel compression-based error adaptor to adjust the parameters and structure of VQC according to the fluctuating quantum noise, such that the VQC can effectively and efficiently adapt to the present quantum noise; (2) building an uncertainty predictor to quantify the deployability of a given pair of VQC and quantum processor, such that users can be aware of performance change; (3) designing a novel visualization tool with scalability to portray the impact of noise on the performance of a given VQC; and (4) the developed toolset is finally integrated into a scientific application, real-time earthquake detection, which can provide insights into identifying real-world tasks where quantum technologies may offer a promising solution. The education impacts of this project include the tutorials on the developed software tools to guide and encourage the domain researchers to leverage the advanced quantum computing cyberinfrastructure; the integration of research to the quantum summer program from the Potomac Quantum Innovation Center for high school seniors; and the development of new undergraduate and graduate courses for quantum workforce training.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.

随着量子计算机不断扩展到更多的量子位，使用量子计算开发实际和现实世界的应用程序已成为量子信息科学家和技术人员的关键前沿，这使各个学科的其他科学家和最终用户受益。量子学习是量子计算和机器学习的结合，也称为变分量子算法（VQA），是最有希望应用于各种实际问题的方法之一。量子学习是一种混合量子经典协议，它使用经典训练优化器通过成本函数来优化变分量子电路 (VQC) 中的参数。然而，量子设备固有的噪声带来了严重的可部署性和可移植性问题，使得优化后的VQC在不同量子计算机之间部署或移植时性能显着下降。更重要的是，量子设备上的噪声随着时间的推移而变化，称为不稳定噪声、波动噪声或噪声漂移，这阻碍了 VQC 在一台量子计算机上不同时间的重复使用，甚至将学习误导到非最佳状态。 VQC训练过程中噪声变化时的路径。该项目旨在通过在量子学习、不确定性预测、噪声抑制和系统可视化方面产生基本理解和实用方法来实现时间可靠的量子学习。使用量子学习在美国能源部资助的超级计算中心的科学应用中评估结果，这些中心提供对各种商业量子计算资源的访问。为了促进实用的量子学习，该项目采用系统和创新的方法来开发一个集成框架，该框架展示了所提出的研究的新颖性、实用价值和领域影响：（1）开发一种新颖的基于压缩的误差适配器根据波动的量子噪声调整VQC的参数和结构，使得VQC能够有效且高效地适应当前的量子噪声； (2) 构建不确定性预测器来量化给定的 VQC 和量子处理器对的可部署性，以便用户能够了解性能变化； (3) 设计一种具有可扩展性的新型可视化工具，以描绘噪声对给定 VQC 性能的影响； （4）开发的工具集最终被集成到科学应用程序中，即实时地震检测，它可以为识别现实世界的任务提供见解，而量子技术可能会在这些任务中提供有前途的解决方案。该项目的教育影响包括开发软件工具的教程，以指导和鼓励领域研究人员利用先进的量子计算网络基础设施；将研究整合到波托马克量子创新中心针对高中生的量子夏季项目；该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。