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Starts 8 June 2025 18:25
Ends 8 June 2025
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Overview
Explore a framework for objective-driven dynamical stochastic fields in quantum AI, presented by Stanford researchers Zhang and Koyejo.
Syllabus
- Introduction to Quantum AI
- Stochastic Processes in Quantum Systems
- Dynamical Systems Theory
- Objective-Driven Approaches
- Framework for Dynamical Stochastic Fields
- Research Insights from Zhang and Koyejo
- Practical Applications and Case Studies
- Hands-On Project
- Conclusion and Future Outlook
- References and Further Reading
Overview of Quantum Computing
Basics of Artificial Intelligence
Intersection of Quantum Computing and AI
Fundamentals of Stochastic Processes
Quantum Stochastic Calculus
Applications in Quantum AI
Introduction to Dynamical Systems
Stability and Bifurcations
Dynamics in Quantum Systems
Defining Objectives in Quantum AI
Optimization Techniques
Case Studies of Objective-Driven Models
Structure and Components of the Framework
Mathematical Formulation
Implementation Strategies
Key Contributions to Quantum AI
Innovations in Stochastic Field Applications
Future Directions and Open Challenges
Real-World Applications of Quantum AI
Case Study Analyses
Evaluating Outcomes in Stochastic Fields
Project Description and Objectives
Tools and Resources
Evaluation Criteria
Recap of Key Learnings
Potential Future Developments in Quantum AI
Opportunities for Further Research
Essential Texts and Papers
Suggested Journals and Articles
Online Resources and Courses
Subjects
Computer Science
