課程簡介
量子力學導論
- 量子力學基本原理。
- 量子態與量子比特。
- 疊加與糾纏。
量子計算基礎
- 量子電路與量子門。
- 量子測量與量子比特操作。
- 量子算法簡介。
量子算法
- 量子算法概述。
- 量子傅里葉變換及其應用。
- Grover算法用於數據庫搜索。
量子AI與機器學習
- 量子機器學習算法。
- 量子神經網絡。
- 量子AI的潛在應用。
量子AI的挑戰與未來
- 量子AI的技術挑戰。
- 倫理考量與社會影響。
- 量子AI的未來趨勢與研究方向。
實驗項目
- 使用Qiskit或類似量子計算框架模擬量子算法。
- 開發基礎量子機器學習模型。
- 小組合作,提出量子AI的創新應用。
總結與下一步
最低要求
- 對線性代數和量子力學有基本瞭解。
- 熟悉Python編程。
受衆
- AI專業人士。
- AI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!
