报告人：陈玉琴 高级研究员（腾讯量子实验室）

时 间：2023年5月5日（星期五）19：00

地 点：腾讯会议，会议号：934 150 593

摘要：

In this talk, I will discuss the challenges in quantum noise manipulation and opportunities in quantum simulation for quantum computing in the noisy intermediate-scale quantum (NISQ) era. Specifically, in the first part, I will present our recent progress on non-Markovian noise on the quantum processor. We propose protocols based on transfer tensor maps and spectral transfer tensor maps to capture non-Markovianity and reconstruct the noise spectral density beyond pure dephasing models. In the second part of this talk, I will introduce the AI-assisted quantum algorithm in accelerating the quantum annealing process, in which we propose a Monte Carlo Tree Search algorithm and QuantumZero to automate the design of annealing schedules. I will also describe a Lyapunov control-assisted quantum algorithm in accelerating quantum imaginary simulation and its realization on a digital quantum computer. At last, I will introduce our newly launched quantum simulation software and its capability of efficient quantum noise simulation and quantum noise mitigation.

简历：

陈玉琴博士是腾讯量子实验室的高级研究员。 2019年于北京大学新葡萄88805官网量子材料中心获得物理学博士学位，而后加入腾讯量子实验室。主要研究领域包括量子信息、量子算法、量子机器学习、人工智能在物理和量子计算中的应用。在Nature Machine Intelligence、Science Bulletin、Physical Review Applied、Quantum等国际知名期刊发表多篇论文。

联系方式：jiangwei.shang@bit.edu.cn

邀请人： 尚江伟

网 址：http:/

承办单位：物理学院、先进光电量子结构设计与测量教育部重点实验室

*Reporter：Yu-Qin Chen (Tencent, Tencent Quantum Laboratory)

*Time：

*Place：

*Contact Person:

*Abstract:

In this talk, I will discuss the challenges in quantum noise manipulation and opportunities in quantum simulation for quantum computing in the noisy intermediate-scale quantum (NISQ) era. Specifically, in the first part, I will present our recent progress on non-Markovian noise on the quantum processor. We propose protocols based on transfer tensor maps and spectral transfer tensor maps to capture non-Markovianity and reconstruct the noise spectral density beyond pure dephasing models. In the second part of this talk, I will introduce the AI-assisted quantum algorithm in accelerating the quantum annealing process, in which we propose a Monte Carlo Tree Search algorithm and QuantumZero to automate the design of annealing schedules. I will also describe a Lyapunov control-assisted quantum algorithm in accelerating quantum imaginary simulation and its realization on a digital quantum computer. At last, I will introduce our newly launched quantum simulation software and its capability of efficient quantum noise simulation and quantum noise mitigation.

*Profile：

Dr. Yu-Qin Chen is a Senior Researcher at Tencent Quantum Laboratory, Tencent, China. She received her Ph.D. in physics from the International Center for Quantum Materials, School of Physics, Peking University in 2019 and then joined Tencent Quantum Laboratory. She is interested in quantum machine learning, artificial intelligence, quantum information science, quantum computation algorithm, and quantum simulation in condensed matter physics and chemical systems. She has published many papers in internationally renowned journals including Nature Machine Intelligence, Science Bulletin, Physical Review Applied, and Quantum.