讲座题目： Quantum Reinforcement Learning（量子增强学习）

主 讲 人：美国霍顿学院终身胡伟教授 (Full Prof. Wei Hu)

时    间：2017年5月24日（周三）15:00-16:00

地    点：6号学院楼402会议室

主办单位：002cc白菜资讯

摘要：

Machine learning can be generally categorized into three classes, supervised, unsupervised, and reinforcement learning. It has played an important role in the analysis of big data and artificial intelligence today. The year of 2016 has witnessed the single greatest AI achievement in human history: AlphaGo, a computer program developed by Google which defeated the best human Go player, a feat no one could ever imagine even just a few years ago. The success of AlphoGo is based on the recent development in machine learning such as deep neural network, convolution network, and reinforcement learning. A quick question after the excitement of AlphaGo phenomenon is what’s next? There is no doubt that computing power is still one of the main bottlenecks of AI advancement, as such turning our attention to quantum computing and quantum algorithms is our natural choice. Based on quantum mechanics, quantum computing is a very different computing paradigm from its classical counterpart as quantum states could be manipulated in parallel. In this talk, I plan to introduce a famous quantum search algorithm, Grover search algorithm, and explain how it could be applied to reinforcement learning. Experiments have shown that even on the classical computers, such application can dramatically reduce the number of iterations to train a machine learning model and the resultant model is more robust to nose in data, a feature very desirable in the world of machine learning. Therefore we do not need to wait for a general purpose quantum computer to become available on our desk, we can take advantage of its power today by using our classical computers.

主讲人简介：

胡伟教授，1978年进入浙江师范大学攻读数学专业本科，1982年进入杭州大学(现浙江大学) 攻读数学专业硕士，1985到1991年在杭州电子科技大学任教。1994年在美国密西根州立大学获数学专业硕士学位，1997年在美国肯塔基大学获数学专业博士和计算机专业硕士学位。同年，到美国霍顿学院任数学和计算机助理教授，于2004年获终身教授，2007年提升为正教授。从2007年起开始数据科学的研究，其中包括艾滋病毒、癌症、流感、社交网络、人工智能等，在这些领域发表了高水平学术论文60余篇。

欢迎全院广大师生踊跃参加。