课程大纲
量子-AI集成简介
- 混合量子-经典智能的动机
- 关键机遇和当前技术障碍
- Google Willow在量子-AI领域的定位
Google Willow架构与能力
- 系统概览与工具链结构
- 支持的量子操作与功能集
- 用于高级实验的API
混合量子-经典模型
- 量子与经典组件之间的任务分配
- 量子增强学习的数据编码策略
- 状态准备与测量工作流程
量子机器学习算法
- 用于AI任务的变分量子电路
- 量子核与特征映射
- 混合模型的优化循环
使用Willow构建量子-AI管道
- 端到端开发混合模型
- 将Willow与TensorFlow Quantum结合
- 测试与验证量子-AI原型
性能优化与资源管理
- 噪声感知的AI模型开发
- 管理混合系统中的计算约束
- 量子-AI性能基准测试
应用与新兴用例
- 量子增强的数据分析
- 量子加速的AI驱动优化
- 跨行业的采用潜力
量子-AI融合的未来趋势
- 大规模量子-AI系统的路线图
- 架构进步与硬件演进
- 塑造量子-AI前沿的研究方向
总结与下一步
要求
- 了解量子计算概念
- 有使用机器学习框架的经验
- 熟悉混合量子-经典工作流程
受众
- AI工程师
- 机器学习专家
- 量子计算研究人员
客户评论 (1)
Quantum computing algorithms and related theoretical background know-how of the trainer is excellent. Especially I'd like to emphasize his ability to detect exactly when I was struggling with the material presented, and he provided time&support for me to really understand the topic - that was great and very beneficial! Virtual setup with Zoom worked out very well, as well as arrangements regarding training sessions and breaks sequences. It was a lot of material/theory to cover in "only" 2 days, wo the trainer had nicely adjusted the amount according to the progress related to my understanding of the topics. Maybe planning 3 days for absolute beginners would be better to cover all the material and content outlined in the agenda. I very much liked the flexibility of the trainer to answer my specific questions to the training topics, even additionally coming back after the breaks with more explanation in case neccessary. Big thank you again for the sessions! Well done!