NSF Award
2311950
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. <br/><br/>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.<br/><br/>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.
