課程簡介
量子-AI集成簡介
- 混合量子-經典智能的動機
- 關鍵機遇和當前技術障礙
- Google Willow在量子-AI領域的定位
Google Willow架構與能力
- 系統概覽與工具鏈結構
- 支持的量子操作與功能集
- 用於高級實驗的API
混合量子-經典模型
- 量子與經典組件之間的任務分配
- 量子增強學習的數據編碼策略
- 狀態準備與測量工作流程
量子機器學習算法
- 用於AI任務的變分量子電路
- 量子核與特徵映射
- 混合模型的優化循環
使用Willow構建量子-AI管道
- 端到端開發混合模型
- 將Willow與TensorFlow Quantum結合
- 測試與驗證量子-AI原型
性能優化與資源管理
- 噪聲感知的AI模型開發
- 管理混合系統中的計算約束
- 量子-AI性能基準測試
應用與新興用例
- 量子增強的數據分析
- 量子加速的AI驅動優化
- 跨行業的採用潛力
量子-AI融合的未來趨勢
- 大規模量子-AI系統的路線圖
- 架構進步與硬件演進
- 塑造量子-AI前沿的研究方向
總結與下一步
最低要求
- 瞭解量子計算概念
- 有使用機器學習框架的經驗
- 熟悉混合量子-經典工作流程
受衆
- AI工程師
- 機器學習專家
- 量子計算研究人員
客戶評論 (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!