Quantum technologies are promising to revolutionize many fields, ranging from fast computation and simulation to precise sensing and secure communication, with their capabilities to outperform classical counterparts. While the performance of quantum devices in the current noisy intermediate-scale quantum era is still limited, exploring intriguing practical applications of various quantum systems and designing feasible path towards achieving a quantum advantage have been an active research area. 

In this talk, I will highlight several opportunities along this direction, including quantum sensing and computation. Specifically, I will start by introducing novel quantum sensing technologies with spin defects in diamond, namely nitrogen-vacancy centers. By transducing signal of interest into magnetic or electric noise, highly sensitive sensors could be designed with intriguing applications in biology and chemistry. In the second part of the talk, I will discuss about near-term quantum algorithms and path towards their potential advantages. In particular, I will focus on recent efforts in exploring possible scaling advantage of quantum approximate optimization algorithm on a classically intractable optimization problem, along with relevant proof-of-principle experimental demonstrations using trapped ion devices.

Brief CV of Dr. Changhao Li:

Dr. Changhao Li earned his Bachelor of Science in Physics from Xi'an Jiaotong University in 2017. He then got his Ph.D. degree in Quantum Science and Engineering in 2022 at Massachusetts Institute of Technology (MIT), where he specialized in the study of solid-state spin defects in diamonds and their applications in quantum sensing and simulation. During his Ph.D., Dr. Li has over ten publications in prestigious journals such as Science, PRL, and Nano Letters as first or co-first author, and his work has been highlighted by media including APS, IEEE and MIT News. In Jan. 2023, Dr. Li joins JPMorgan Quantum team, where he is working on novel quantum algorithms including quantum optimization and quantum machine learning.