（ByteResearch）

摘 要：

At present, Moore's Law is gradually failing, and various new computing architectures are emerging one after another. Quantum computing is likely to be a revolutionary technology in the future and has recently exhibited great potentials in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimizationtransition oxygen system such as NiO which only requires 20 qubits which may require almost 10k qubits vice versa. Besides, I will also briefly discuss other theoretical research in quantum chemistry simulation, the experimental realization of mainstream quantum systems, and future research trends and difficulties.

However, quantum computing is in the era of noise intermediate-scale (NISQ), with the number of qubits up to (50-1000), limited coherence time, and gate fidelity. Progress has been made in simulating small molecules with no more than 20 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from limitations of the size and the fidelity of near-term quantum hardware, how to accurately simulate large scale molecules and materials remains a challenge.

In this talk, we shall present our work towards the larger scale and realistic chemistry simulation. Particularly, combine with quantum embedding theory, density matrix embedding theory as an example, we have greatly enhanced the ability of the current quantum device to simulate complex transition oxygen system such as NiO which only requires 20 qubits which may require almost 10k qubits vice versa. Besides, I will also briefly discuss other theoretical research in quantum chemistry simulation, the experimental realization of mainstream quantum systems, and future research trends and difficulties.

参考文献：

[1]. Cao, Changsu, Jinzhao Sun, Xiao Yuan, Han-Shi Hu, Hung Q. Pham, and Dingshun Lv. npj Computational Materials 9 (1), 78

[2]. Cao, Changsu, Jiaqi Hu, Wengang Zhang, Xusheng Xu, Dechin Chen, Fan Yu, Jun Li, Han-Shi Hu, Dingshun Lv, and Man-Hong Yung. Phys. Rev. A 105, 6 (2022): 062452.

[3]. Y. Fan, C. Cao, X. Xu, Z. Li, D. Lv, M.-H. Yung, J. Phys. Chem. Lett. 2023, 14, 43, 9596–9603 [4]. Li, Weitang, Zigeng Huang, Changsu Cao, Yifei Huang, Zhigang Shuai, Xiaoming Sun, Jinzhao Sun, Xiao Yuan, and Dingshun Lv. Chemical Science 13, 31 (2022): 8953-8962.

报告人简介：

吕定顺，博士，字节跳动ByteDance Research，量子计算和量子化学方向研究员, 技术负责人。本科就读于哈尔滨工业大学应用物理学专业，2012年本科毕业后，保送至清华大学交叉信息研究院，师从国际知名离子阱实验专家Kihwan Kim，从事量子计算和量子模拟方面的研究，至今在量子计算、量子模拟领域等已经有10+年研究经验。2018年博士毕业后，加入华为2012实验室，担任量子计算和量子算法研究员，专精并聚焦在量子软件和算法研究领域。在华为工作期间主要聚焦基于变分本征求解（VQE）的量子多体模拟（量子化学模拟，Hubbard model，Schwinger model，Heisenberg model模拟）以及量子近似算法研究（QAOA）。2021年4月，入职字节跳动，继续聚焦量子计算和量子化学方向的研究。2021年8月，加入CCF量子计算专业组，成为首批执行委员。

研究成果：在Nature Physics, Nature Communication, PRX, PRL，Chemical Science, npj ComuputationalMaterials, JCTC, JPCL，QST, PRA等国际知名期刊已发表论文10+篇，Hindex为11，Google论文累计引用大于1000次。在公司主导申请10+专利。

研究兴趣：量子计算，量子化学，大规模量子化学模拟，复杂强关联体系模拟等。

邀请人：任新国（82649603）