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Towards Practical Simulation of Realistic Chemical Systems on Near-term Quantum Computers
01/11
2022
- Title Towards Practical Simulation of Realistic Chemical Systems on Near-term Quantum Computers
- Speaker 吕定顺 博士 (字节跳动量子计算部门)
- Date 2022年1月11日 10:00
- Venue 南楼6620
Abstract
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 optimization.
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, such as LiH and hydrogen chains of up to 12 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 realistic molecules remains a challenge.
In this talk, I will present our work towards the larger scale and realistic chemistry simulation. 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.
个人简介:
吕定顺,博士,字节跳动AI Lab实验室,量子计算和量子化学方向研究员。本科就读于哈尔滨工业大学应用物理学专业,2012年毕业后,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KihwanKim,从事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9年研究经验。博士期间,曾在Nature Physics,PRX,Nature Communication,PRL,PRA等国际知名期刊发表论文7篇,Hindex为8,Google论文累计引用大于680次。2018年博士毕业后,入职华为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)量子近似算法研究(QAOA)和基于谷歌随机量子线路(RQC)的张量网络模拟。相关主要成果已经在2019和2020届HuaweiConnect大会上发布,并有对应的软件包HiQFermion、HiQOptimizer和HiQTensor。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。在公司工作期间,累计主导和参与(在)申请9项专利,主导完成基于VQE的业界最大规模C2H4分子模拟,1000节点的Max-Cut等组合优化问题展示和高效率的谷歌随机线路模拟。目前基于量子计算和结合quantum embedding theory 实现144比特的C18 的模拟。相关工作在投稿中。
所内联系人: 张 潘