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

贾斯汀·雷耶斯被授予NSF数学和物理科学上升博士后研究奖学金，在中央佛罗里达大学进行研究和教育计划。Reyes将利用机器学习算法来检测、分类和减轻量子电路中各种噪声通道的错误。我们的目标是开发和优化算法，以减轻量子变分算法中的错误，这些算法可在噪声中间尺度量子（NISQ）设备上实现。 沿着这项研究，雷耶斯打算通过美国物理学会物理学桥梁项目，为代表性不足的少数民族学生担任导师。这项提议对科学界的贡献是巨大的。在量子计算方面，这项研究的成功将推动目前在NISQ器件上实现量子计算的进展，为表征和减轻量子电路中的噪声提供一种新的改进方法。在量子多体系统和量子化学领域内，将获得对分子能量基态的更精确的近似。在计算机科学中，这一提议的结果将通过提供更准确的近似解来推进组合优化的研究，同时可能为持续改进铺平道路。在数学方面，将提供对噪声和误差传播之间的相关性的更深入理解，从而产生潜在的噪声表征新模型。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进行评估，被认为值得支持。

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.