Neural Network Quantum Error Mitigation for Algorithms on Near-Term Intermediate Scale Quantum Devices

Reyes, Justin, Orlando FL

Investigators

Abstract

Justin Reyes is awarded an NSF Mathematical and Physical Sciences Ascending Postdoctoral Research Fellowship to conduct a program of research and education at the University of Central Florida. Reyes will utilize machine learning algorithms for the detection, categorization, and mitigation of errors from various noise channels in quantum circuits. The goal will be to develop and optimize algorithms which mitigate errors in quantum variational algorithms that are implementable on Noisy Intermediate-Scale Quantum (NISQ) devices. Along with the research, Reyes intends to act as a mentor to underrepresented minority students through the American Physical Society Bridge Program in Physics. The contributions of this proposal onto the scientific community are numerous. In quantum computation, the success of this proposed research would advance the progress of current implementations of quantum computation on NISQ devices, providing a novel and improved means of characterizing and mitigating noise in quantum circuits. Within the field of quantum many-body systems and quantum chemistry, more accurate approximations to the ground states of molecular energies would be obtained. In computer science, the results of this proposal would advance the study of combinatorial optimization by providing more accurate approximate solutions, while potentially paving a path forward for continued improvement. Within mathematics, a deeper understanding of the correlations between noise and the spread of error would be provided, leading to potentially new models of noise characterization. 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.